Analyses of Eurobarometer Waves for the Paper Constitutional Structures and Parties’ Perceptions on E-Voting throughout Europe

This is an R Markdown document describing our data analysis on Eurobarometer surveys for the comparison on whether Europeans favour e-voting.

Our leading research question is: Why is e-voting exciting both for parties and Europe, and why is it needed to compare constitutions and perceptions in parties. Is e-voting even part of the digital agenda if the EU wants to align with its member states?

Before doing so, we need to prepare our settings, environments, load packages, and transform varibles.

Prerequisites

setwd("~/Google Drive/E-Voting Constitutions/Data collection/survey/analysis")
getwd()
## [1] "/Users/isabelleborucki/Google Drive/E-Voting Constitutions/Data collection/survey/analysis"

Reading in Data

euro2002 <- data.frame(read_sav("ZA3388_v1-1-0.sav"))

euro2016 <- data.frame(read_sav("ZA6653_v1-0-0.sav"))

euro2018 <- data.frame(read_sav("ZA7484_v1-0-0.sav"))

We import the 2001 Flash and Specials Eurobarometer on Impacts of New Technologies, Employment and Social Affairs, and Disabilities, the 2016 Flash Eurobarometer 431 (Electoral Rights, wave 3), and the Eurobarometer 90.1: Democracy and elections from 2018. All three entails items measuring e-voting or similar concepts.

For those, we chose several questions throughout the surveys:

2016 Flash Eurobarometer 431 (Electoral Rights, wave 3) https://search.gesis.org/research_data/ZA6653 - Q6: “EU-Bürger, die in einem anderen EU-Land leben, haben das Recht, bei Europa- oder Kommunalwahlen im entsprechenden Land zu wählen. Wenn Sie in einem anderen EU-Land als in Ihrem Herkunftsland leben würden, inwieweit stimmen Sie zu oder nicht zu, dass Ihnen folgende Dinge die Teilnahme an dortigen Wahlen erleichtern würden?” -—> Antwortmöglichkeit: “Die Möglichkeit, elektronisch oder online zu wählen” - Q8: “Wenn Sie die Möglichkeit hätten, elektronisch, online oder per Briefwahl zu wählen, wie besorgt wären Sie hinsichtlich der folgenden Aspekte?”

2018 - Eurobarometer 90.1: Parlemeter 2018, Democracy and elections and Antimicrobial resistance (2018) 90.1: Democracy and elections https://search.gesis.org/research_data/ZA7484 - QB3: “Und wenn Sie in einem EU‐Land leben oder leben würden, das nicht Ihr Herkunftsland ist, welche der folgenden Optionen wäre Ihre bevorzugte Art, bei den nationalen Wahlen Ihres Herkunftslandes Ihre Stimme abzugeben?” -—> Antwortmöglichkeit: “Elektronisch oder online Ihre Stimme abzugeben”

  • QB7: “Stellen Sie sich nun vor, Sie könnten elektronisch, online oder postalisch wählen. Wie besorgt bzw. nicht besorgt wären Sie in Bezug auf jeden der folgenden Aspekte?”

trust: ist mehr oder weniger standard- und regelmäßig erhoben als „Trust in institutions …“ bspw. für: European Union, national government, national parliament, political parties Ausprägungen: „tend to trust“, „tend not to trust“ & „don’t know“

Data preparation

First, we look into the raw data on respective variables, we are interested in. Besides our most interesting variables on e-voting, we also want to look into sociodemographics: sex, age, education, and internet usage as possible proxy for a prevalence towards e-voting. Probably, party identification might be of interest too.

Euro 2002

First, we take a look into the variables on sex, then age and education and the availability of a landline, mobile phone, and internet.

Sex

# sex
table(euro2002$v298)
## 
##    1    2 
## 8322 8781
euro2002$sex <- NA
euro2002$sex <- euro2002$v298
table(euro2002$sex)
## 
##    1    2 
## 8322 8781

We see, in 2002, we have 8322 males and 8781 females in our sample.

Age

# age exact NOTE: People from this survey who were 15 then are now 36!
table(euro2002$v299)
## 
##  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34 
## 177 242 256 290 324 240 291 302 290 314 293 309 299 302 297 338 306 325 307 392 
##  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54 
## 341 360 329 354 351 323 262 298 310 269 298 247 271 287 276 336 227 270 257 273 
##  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72  73  74 
## 281 247 242 227 243 293 158 244 267 212 288 223 220 191 172 247 169 197 155 131 
##  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89  90  91  93  94  95 
## 159 109 100  89  88  91  45  42  25  31  16  17  10  12   7   9   6   2   2   1 
##  98  99 
##   1   1
euro2002$age <- NA
euro2002$age <- (euro2002$v299 + 21) # today's age of respondents (or their death - we now would have people older than 120 years)
# thus, we exclude people older than 100
euro2002 <- euro2002[euro2002$age < 101,] # but still, we might have some death in here

Education

table(euro2002$v296)
## 
##    1    2    5    6    7    8    9   10   11   12   13   14   15   16   17   18 
##    1    1    1  111   13   39   52  226  186  548  333 1447 1360 1920 1253 2062 
##   19   20   21   22   23   24   25   26   27   28   29   30   31   32   33   34 
## 1067  907  613  530  419  406  345  256  158  124   62  130   46   55   33   39 
##   35   36   37   38   39   40   41   42   43   44   45   46   48   50   51   52 
##   58   28   22   29   28    8    4    3    6    6   10    3    5    8    1   10 
##   53   54   58   62   98 
##    1    1    1    1 1757
euro2002$educ <- NA
euro2002$educ <- euro2002$v296

table(euro2002$educ)
## 
##    1    2    5    6    7    8    9   10   11   12   13   14   15   16   17   18 
##    1    1    1  111   13   39   52  226  186  548  333 1447 1360 1920 1253 2062 
##   19   20   21   22   23   24   25   26   27   28   29   30   31   32   33   34 
## 1067  907  613  530  419  406  345  256  158  124   62  130   46   55   33   39 
##   35   36   37   38   39   40   41   42   43   44   45   46   48   50   51   52 
##   58   28   22   29   28    8    4    3    6    6   10    3    5    8    1   10 
##   53   54   58   62   98 
##    1    1    1    1 1757

telephone available (no internet var)

table(euro2002$v374)
## 
##     1     2 
## 15318  1460
euro2002$tech <- NA
euro2002$tech <- euro2002$v374

Nationality

table(euro2002$isocntry) # contains the isocountries where the survey took place and the residency of respondents
## 
##     AT     BE   DE-E   DE-W     DK     ES     FI     FR GB-GBN GB-NIR     GR 
##   1048   1017    997   1007    975    977    989    993    973    313    987 
##     IE     IT     LU     NL     NO     PT     SE 
##    986    987    594   1002    988    979    973
# since no variable containing all nations is available, it is needed to transform the single variables into one.

euro2002$country <- NA
# euro2002$country <- do.call(paste,
#                             c(euro2002[c("v23","v24","v25","v26","v27","v28",
#                                          "v29","v30","v31","v32","v33","v34",
#                                          "v35","v36","v37")],
#                               sep="-"))

euro2002$country[euro2002$v23 == 1] <- 23
euro2002$country[euro2002$v24 == 1] <- 24
euro2002$country[euro2002$v25 == 1] <- 25
euro2002$country[euro2002$v26 == 1] <- 26
euro2002$country[euro2002$v27 == 1] <- 27
euro2002$country[euro2002$v28 == 1] <- 28
euro2002$country[euro2002$v29 == 1] <- 29
euro2002$country[euro2002$v30 == 1] <- 30
euro2002$country[euro2002$v31 == 1] <- 31
euro2002$country[euro2002$v32 == 1] <- 32
euro2002$country[euro2002$v33 == 1] <- 33
euro2002$country[euro2002$v34 == 1] <- 34
euro2002$country[euro2002$v35 == 1] <- 35
euro2002$country[euro2002$v36 == 1] <- 36
euro2002$country[euro2002$v37 == 1] <- 37

euro2002$country <- factor(euro2002$country,
                           labels = c("Belgium",
                                    "Denmark",
                                    "Germany",
                                    "Greece",
                                    "Spain",
                                    "France",
                                    "Ireland",
                                    "Italy",
                                    "Luxembourg",
                                    "Netherlands",
                                    "Portugal",
                                    "United Kindom",
                                    "Austria",
                                    "Sweden",
                                    "Finland"))

table(euro2002$country) # country of respondents' residency
## 
##       Belgium       Denmark       Germany        Greece         Spain 
##           956           974          2014           989           982 
##        France       Ireland         Italy    Luxembourg   Netherlands 
##          1035          1027          1074           426          1017 
##      Portugal United Kindom       Austria        Sweden       Finland 
##          1059          1244          1037           963          1000
table(euro2002$isocntry) # country of respondents' origin
## 
##     AT     BE   DE-E   DE-W     DK     ES     FI     FR GB-GBN GB-NIR     GR 
##   1048   1017    997   1007    975    977    989    993    973    313    987 
##     IE     IT     LU     NL     NO     PT     SE 
##    986    987    594   1002    988    979    973

Euro 2016

we do the same for the 2016 wave.

sex

euro2016$sex <- NA 
euro2016$sex <- euro2016$d2

table(euro2016$d2)
## 
##     1     2 
## 11355 15202
table(euro2016$sex)
## 
##     1     2 
## 11355 15202

Age

euro2016$age <- NA
euro2016$age <- (euro2016$d1 + 6) # today's age of respondents 

# age exact NOTE: People from this survey who were 15 then are now 36!

Education

# education: Age when finished education
table(euro2016$d4)
## 
##    0    2    4    5    6    7    8    9   10   11   12   13   14   15   16   17 
##   53    5    4    4    2    6    9   17   74   85  226  167  987 1068 1456 1362 
##   18   19   20   21   22   23   24   25   26   27   28   29   30   31   32   33 
## 4209 2490 1939 1468 1749 1636 1626 1314  724  454  353  146  282   70  128   79 
##   34   35   36   37   38   39   40   41   42   43   44   45   46   47   48   49 
##   34  105   48   35   37   28   86   24   23   12   20   42   11   19   18   10 
##   50   51   52   53   54   55   56   57   58   59   60   61   62   63   64   65 
##   50   14   13    6    9   15   10    3    6    4   18    3    5    2    1    3 
##   66   67   68   70   72   73   76   83   97   98   99 
##    2    3    1    2    2    1    1    1  173 1176  289
euro2016$educ <- NA
euro2016$educ <- euro2016$d4
table(euro2016$educ)
## 
##    0    2    4    5    6    7    8    9   10   11   12   13   14   15   16   17 
##   53    5    4    4    2    6    9   17   74   85  226  167  987 1068 1456 1362 
##   18   19   20   21   22   23   24   25   26   27   28   29   30   31   32   33 
## 4209 2490 1939 1468 1749 1636 1626 1314  724  454  353  146  282   70  128   79 
##   34   35   36   37   38   39   40   41   42   43   44   45   46   47   48   49 
##   34  105   48   35   37   28   86   24   23   12   20   42   11   19   18   10 
##   50   51   52   53   54   55   56   57   58   59   60   61   62   63   64   65 
##   50   14   13    6    9   15   10    3    6    4   18    3    5    2    1    3 
##   66   67   68   70   72   73   76   83   97   98   99 
##    2    3    1    2    2    1    1    1  173 1176  289

Telephone, landline and technical availability

# telephone, landline available (no internet var)
table(euro2016$d20)
## 
##     1     2 
## 20187  6370
# mobile device
table(euro2016$d18)
## 
##     1     2 
## 24929  1628
table(euro2016$d18_d20) # landline and mobile combined: 1=mobile only 2=landline only 3=mobile and landline
## 
##     1     2     3 
##  6370  1628 18559
# technical availability
euro2016$tech <- NA
euro2016$tech <- euro2016$d18_d20 # we now have a new variable containing the information of the old variable

Nationality

table(euro2016$isocntry) # contains the isocountries where the survey took place
## 
##   AT   BE   BG   CY   CZ   DE   DK   EE   ES   FI   FR   GB   GR   HR   HU   IE 
## 1002 1005 1000  502 1000 1000 1008 1000 1001 1001 1011 1002 1000 1000 1005 1000 
##   IT   LT   LU   LV   MT   NL   PL   PT   RO   SE   SI   SK 
## 1000 1000  505 1002  501 1001 1000 1001 1005 1000 1004 1001
table(euro2016$country) # country of respondents' residency
## 
##    1    2    3    4    5    6    7    8    9   11   12   13   16   17   18   19 
## 1011 1005 1001 1000 1000  505 1008 1000 1002 1000 1001 1001 1001 1000 1002  502 
##   20   21   22   23   24   25   26   27   28   29   30   32 
## 1000 1000 1005 1002 1000  501 1000 1001 1004 1000 1005 1000

Euro 2018

And now for the last wave of 2018

sex

euro2018$sex <- NA
euro2018$sex <- euro2018$d10
table(euro2018$sex)
## 
##     1     2 
## 12370 15104
table(euro2018$d10)
## 
##     1     2 
## 12370 15104

Age and Education

# age exact NOTE: People from this survey who were 15 then are now 36!

euro2018$age <- NA
euro2018$age <- (euro2018$d11 + 4) # today's age of respondents 

# education: Age when finished education

table(euro2018$d8)
## 
##    0    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
##   74   12   10    6    1    6   10   21   42  170  162  453  210 1234 1514 1871 
##   17   18   19   20   21   22   23   24   25   26   27   28   29   30   31   32 
## 1789 5345 2728 1459 1119 1219 1245 1216 1046  497  296  281  117  173   70   89 
##   33   34   35   36   37   38   39   40   41   42   43   44   45   46   47   48 
##   56   35   74   21   37   31   30   60   16   27   18   17   36   18   18   24 
##   49   50   51   52   53   54   55   56   57   58   59   60   61   62   63   64 
##   11   25   11   15    6    7   25   13    7    8   13   12    5    7    5    4 
##   65   66   67   68   69   70   71   72   73   74   75   76   77   78   79   80 
##   11    5    7    6    4    4    7    3    3    3    2    2    1    3    1    3 
##   81   82   83   84   88   89   97   98   99 
##    3    2    2    1    1    1  198 1641  383
euro2018$educ <- NA
euro2018$educ <- euro2018$d8

Telephone, landline and technical availability

# internet use total
table(euro2018$d62t)
## 
##     1     2     3     4 
## 19081  2686  5180   527
# fixed telephone/land line
table(euro2018$d43a)
## 
##     1     2 
## 12629 14845
# mobile device
table(euro2018$d43b)
## 
##     1     2 
## 25954  1520
# landline and mobile 
table(euro2018$d43t) # landline and mobile combined: 1=mobile only 2=landline only 3=mobile and landline
## 
##     1     2     3     4 
## 14324   999 11630   521
euro2018$tech <- NA
euro2018$tech <- euro2018$d43t
table(euro2018$tech)
## 
##     1     2     3     4 
## 14324   999 11630   521
# netuse! overall usage of the internet 
table(euro2018$netuse)
## 
##     1     2     3     4     5     6     7 
## 19081  1626   488   195   377  5180   527

Left-right self-placement

table(euro2018$d1)
## 
##    1    2    3    4    5    6    7    8    9   10   97   98 
## 1213  881 2055 2123 6351 2317 2180 1803  629 1436 1558 2534

Nationality

table(euro2018$isocntry) # contains the isocountries where the survey took place
## 
##   AT   BE   BG   CY   CZ DE-E DE-W   DK   EE   ES   FI   FR   GB   GR   HR   HU 
## 1007 1018 1040  503 1012  501 1006 1015 1017 1009 1011 1027 1000 1015 1050 1018 
##   IE   IT   LT   LU   LV   MT   NL   PL   PT   RO   SE   SI   SK 
## 1001 1029 1007  506 1001  509 1044 1034 1016 1007 1011 1018 1042
table(euro2018$country) # country of respondents' residency
## 
##    1    2    3    4    5    6    7    8    9   11   12   13   14   16   17   18 
## 1027 1018 1044 1006 1029  506 1015 1001 1000 1015 1009 1016  501 1011 1011 1007 
##   19   20   21   22   23   24   25   26   27   28   29   30   32 
##  503 1012 1017 1018 1001 1007  509 1034 1042 1018 1040 1007 1050

Having listed all those interesting describing variables, we now start over investigating these in more detail, in combination with the variables on e-voting.

Electronic Voting Variables

2002

Calculate meanscore for technical issues: How do people rely on technical assessments and getting in contact with politics?

# meanscore on statements towards new technology
#calculation mean score v53-v56

euro2002$v53 [euro2002$v53<1] <- NA # People should have more access to political representatives via the Internet
euro2002$v54 [euro2002$v54<1] <- NA # There should be more direct access to the civil service via the Internet
euro2002$v55 [euro2002$v55<1] <- NA # On-line voting should be used for elections and referenda
euro2002$v56 [euro2002$v56<1] <- NA # New technologies will create more possibilities for expressing your opinion

euro2002$newtech <- rowMeans(euro2002[, c("v53","v54","v55","v56")], na.rm = TRUE)

table(euro2002$newtech)
## 
##                1             1.25 1.33333333333333              1.5 
##             1268              646               48              763 
## 1.66666666666667             1.75                2             2.25 
##               70              924             2697             1433 
## 2.33333333333333              2.5 2.66666666666667             2.75 
##              152             1517              173             1160 
##                3             3.25 3.33333333333333              3.5 
##             1773              677              122              667 
## 3.66666666666667             3.75                4             4.25 
##              100              365              743              175 
## 4.33333333333333              4.5 4.66666666666667             4.75 
##               42              130               17               49 
##                5 
##              465

calculate dummy for e-voting from item “On-line voting should be used for elections and referenda”, entailed in the battery v53 to v56

Recode into dummy: On-line voting should be used for elections and referenda

# RECODE into dummy

euro2002$evotedmy [as.numeric(euro2002$v55) ==1] <- 1
euro2002$evotedmy [as.numeric(euro2002$v55) ==2] <- 1
euro2002$evotedmy [as.numeric(euro2002$v55) ==3] <- NA # set here since this would distort the data strongly towards e-voting which is not intended by putting a neutral point to either the one or the other side of the scale.
euro2002$evotedmy [as.numeric(euro2002$v55) ==4] <- 0
euro2002$evotedmy [as.numeric(euro2002$v55) ==5] <- 0

table(euro2002$evotedmy)
## 
##    0    1 
## 6770 6580

recode other items into factors or dummy variables (depending on the variable scale)

RECODE into dummy: People should have more access to political representatives via the Internet

# RECODE into dummy: People should have more access to political representatives via the Internet

euro2002$effdmy [as.numeric(euro2002$v53) ==1] <- 1
euro2002$effdmy [as.numeric(euro2002$v53) ==2] <- 1
euro2002$effdmy [as.numeric(euro2002$v53) ==3] <- NA # set here since this would distort the data strongly towards e-voting which is not intended by putting a neutral point to either the one or the other side of the scale. 
euro2002$effdmy [as.numeric(euro2002$v53) ==4] <- 0
euro2002$effdmy [as.numeric(euro2002$v53) ==5] <- 0

table(euro2002$effdmy)
## 
##    0    1 
## 3097 9581

2016

recode into dummy: E-voting EU-Bürger, die in einem anderen EU-Land leben, haben das Recht, bei Europa- oder Kommunalwahlen im entsprechenden Land zu wählen. Wenn Sie in einem anderen EU-Land als in Ihrem Herkunftsland leben würden, inwieweit stimmen Sie zu oder nicht zu, dass Ihnen folgende Dinge die Teilnahme an dortigen Wahlen erleichtern würden? 4 = Die Möglichkeit, elektronisch oder online zu wählen

# RECODE into dummy

# Facilitating voting procedures through e-voting
#[1] "Q7 Being able to vote electronically or online"
table(euro2016$q7_2)
## 
##     1     2     3     4     5 
## 13537  5626  2234  4471   689
euro2016$evotedmy [as.numeric(euro2016$q7_2) ==1] <- 1
euro2016$evotedmy [as.numeric(euro2016$q7_2) ==2] <- 1
euro2016$evotedmy [as.numeric(euro2016$q7_2) ==3] <- 0
euro2016$evotedmy [as.numeric(euro2016$q7_2) ==4] <- 0
euro2016$evotedmy [as.numeric(euro2016$q7_2) ==5] <- NA # DK

table(euro2016$evotedmy)
## 
##     0     1 
##  6705 19163

2018

in 2018 we have the problem, that not e-voting as such was measured but the tendencies of people to vote for expats (see below). Thus we here have another variable that might be somehow biased in the sense that it was asked: “Imagine now that you were able to vote electronically, online or by post. How concerned or not would you be about each of the following?” With this asking about concerns on e-voting, a neutral measurement is not really possible. We work further here with the exclusion of vulnerable groups as potential DV on evoting processes, since this best fits to the literature that is pro or contra evoting.

Difficulties of use for some people, such as people with disabilities or older people # ELECTRONIC/ONLINE/MAIL VOTING CONCERNS - POTENTIAL FRAUD/CYBERATTACK”

# RECODE into dummy
# qb3 "And if you live or were to live in an EU country other than your country of origin, which of
#the following would be your preferred way of voting in the national elections of your
#country?"

# euro2018$evotedmy <- NA 
# euro2018$evotedmy [as.numeric(euro2018$qb3) ==1] <- 0
# euro2018$evotedmy [as.numeric(euro2018$qb3) ==2] <- 1
# euro2018$evotedmy [as.numeric(euro2018$qb3) ==3] <- 0
# euro2018$evotedmy [as.numeric(euro2018$qb3) ==4] <- 0
# euro2018$evotedmy [as.numeric(euro2018$qb3) ==5] <- 0
# euro2018$evotedmy [as.numeric(euro2018$qb3) ==6] <- NA

# RECODE into dummy
#[1] "ELECTRONIC/ONLINE/MAIL VOTING CONCERNS - DIFFICULT TO USE"

euro2018$evotedmy [as.numeric(euro2018$qb7_1) ==1] <- 0
euro2018$evotedmy [as.numeric(euro2018$qb7_1) ==2] <- 0
euro2018$evotedmy [as.numeric(euro2018$qb7_1) ==3] <- 1
euro2018$evotedmy [as.numeric(euro2018$qb7_1) ==4] <- 1
euro2018$evotedmy [as.numeric(euro2018$qb7_1) ==5] <- NA


# RECODE into dummy
# ELECTRONIC/ONLINE/MAIL VOTING CONCERNS - POTENTIAL FRAUD/CYBERATTACK"

euro2018$evotedmy2 [as.numeric(euro2018$qb7_2) ==1] <- 0
euro2018$evotedmy2 [as.numeric(euro2018$qb7_2) ==2] <- 0
euro2018$evotedmy2 [as.numeric(euro2018$qb7_2) ==3] <- 1
euro2018$evotedmy2 [as.numeric(euro2018$qb7_2) ==4] <- 1
euro2018$evotedmy2 [as.numeric(euro2018$qb7_2) ==5] <- NA

table(euro2018$evotedmy)
## 
##     0     1 
## 17495  8752
table(euro2018$evotedmy2)
## 
##     0     1 
## 17586  8419
table(euro2018$evotedmy3)
## < table of extent 0 >
attributes(euro2018$qb7_2)
## $label
## [1] "ELECTRONIC/ONLINE/MAIL VOTING CONCERNS - POTENTIAL FRAUD/CYBERATTACK"
## 
## $format.spss
## [1] "F1.0"
## 
## $class
## [1] "haven_labelled" "vctrs_vctr"     "double"        
## 
## $labels
##       Very concerned   Somewhat concerned   Not very concerned 
##                    1                    2                    3 
## Not at all concerned                   DK 
##                    4                    5
attributes(euro2018$qb7_1)
## $label
## [1] "ELECTRONIC/ONLINE/MAIL VOTING CONCERNS - DIFFICULT TO USE"
## 
## $format.spss
## [1] "F1.0"
## 
## $class
## [1] "haven_labelled" "vctrs_vctr"     "double"        
## 
## $labels
##       Very concerned   Somewhat concerned   Not very concerned 
##                    1                    2                    3 
## Not at all concerned                   DK 
##                    4                    5
attributes(euro2018$qb3)
## $label
## [1] "IF LIVING IN OTHER EU COUNTRY - PREF WAY VOTING NAT ELEC"
## 
## $format.spss
## [1] "F1.0"
## 
## $class
## [1] "haven_labelled" "vctrs_vctr"     "double"        
## 
## $labels
## Voting in the embassy or consulate of your country of origin 
##                                                            1 
##                              Voting electronically or online 
##                                                            2 
##                                               Voting by post 
##                                                            3 
##                                          Other (SPONTANEOUS) 
##                                                            4 
##                             You would not vote (SPONTANEOUS) 
##                                                            5 
##                                                           DK 
##                                                            6

Now, evote dummy could be used as DV for 2018, if we want to do so, with all problems mentioned above.

Expats Variables

Being an expat may affect how respondents perceive voting via internet or not. The following variables test for prefering voting online when living abroad - so, the question itself is conditional to living outside of one’s statehood country. Maybe, this is an alternative as DV, since we have this variables in all waves.

2002

For 2002 we have no expat voting variable, but we have concerns on new technology, which of course, measures another construct, but could give an impression of how people felt in these days about new technology.

# RECODE into dummy
#Do you have any concerns about on-line voting?
# Yes, some people would be excluded from voting

euro2002$exptdmy [as.numeric(euro2002$v60) ==1] <- 1
euro2002$exptdmy [as.numeric(euro2002$v60) ==0] <- 0

table(euro2002$exptdmy)
## 
##    0    1 
## 6517 4631
# attributes(euro2018$qb3)

2016

Also, for 2016 we recode living outside the homeland, but in another EU country into a dummy variable. Here, the variable was slightly changed to asking about the preferred way to vote when living in the EU but not in the statehood nation.

# IF LIVING IN OTHER EU COUNTRY - PREF WAY VOTING NAT ELEC

# RECODE into dummy
# IF LIVING IN OTHER EU COUNTRY - PREF WAY VOTING NAT ELEC
attributes(euro2016$q6_4)
## $label
## [1] "Q6 Being able to vote electronically or online"
## 
## $format.spss
## [1] "F1.0"
## 
## $class
## [1] "haven_labelled" "vctrs_vctr"     "double"        
## 
## $labels
##    Strongly agree     Tend to agree  Tend to disagree Strongly disagree 
##                 1                 2                 3                 4 
##        Don't know 
##                 5
euro2016$exptdmy [as.numeric(euro2016$q6_4) ==1] <- 0
euro2016$exptdmy [as.numeric(euro2016$q6_4) ==2] <- 1
euro2016$exptdmy [as.numeric(euro2016$q6_4) ==3] <- 0
euro2016$exptdmy [as.numeric(euro2016$q6_4) ==4] <- 0
euro2016$exptdmy [as.numeric(euro2016$q6_4) ==5] <- NA #DK
euro2016$exptdmy [as.numeric(euro2016$q6_4) ==6] <- NA #NA

table(euro2016$exptdmy)
## 
##     0     1 
## 19651  6059

2018

Also, for 2018 we recode living outside the homeland, but in another EU country, into a dummy variable.

# IF LIVING IN OTHER EU COUNTRY - PREF WAY VOTING NAT ELEC $labels Voting in the embassy or consulate of your country of origin 1 Voting electronically or online 2 Voting by post 3 Other (SPONTANEOUS) 4 You would not vote (SPONTANEOUS) 5 DK 6 Here, we take the second statement to compose the dummy variable.

# RECODE into dummy
# IF LIVING IN OTHER EU COUNTRY - PREF WAY VOTING NAT ELEC: E-voting/online voting.
table(euro2018$qb3)
## 
##     1     2     3     4     5     6 
##  7456 11774  4621   146  2034  1443
euro2018$exptdmy [as.numeric(euro2018$qb3) ==1] <- 0
euro2018$exptdmy [as.numeric(euro2018$qb3) ==2] <- 1
euro2018$exptdmy [as.numeric(euro2018$qb3) ==3] <- 0
euro2018$exptdmy [as.numeric(euro2018$qb3) ==4] <- 0
euro2018$exptdmy [as.numeric(euro2018$qb3) ==5] <- NA
euro2018$exptdmy [as.numeric(euro2018$qb3) ==6] <- NA

table(euro2018$exptdmy)
## 
##     0     1 
## 12223 11774
# attributes(euro2018$qb3)

DV

We now think of what our DV could be in several models. First, the statement that online voting should be used for elections from the 2002 wave seems appropriate. We recode our already existing dummy into a new variable, now containing our DV. Unfortunately, this variable is only available in this wave, making comparision impossible.

2002: Online voting should be used for elections and referenda, we herefore use our dummy variable.

DV 2002

euro2002$DV <- NA
euro2002$DV <- euro2002$evotedmy

DV 2016

The same is appropriate for 2016. 2016: Online voting should be used for elections and referenda, we herefore use our dummy variable.

euro2016$DV <- NA
euro2016$DV <- euro2016$evotedmy

DV 2018

And for 2018. Here, we have the afore-mentioned problem that not the same variable was used as in the surveys before which does not allow for comparison. 2018: Online voting should be used for elections and referenda, we herefore use our dummy variable.

euro2018$DV1 <- NA
euro2018$DV1 <- euro2018$evotedmy

euro2018$DV2 <- NA
euro2018$DV2 <- euro2018$evotedmy2

Analysis

Some plotting

2002

ggplot(euro2002,
       aes(x=DV, y=exptdmy))+
  geom_point(position="jitter", alpha=0.9)+
  #stat_summary(fun.y = mean, colour = "darkblue")+
  #stat_summary(fun.y = mean, colour = "darkgrey")+
  labs(x="Online voting should be used for elections", y = "Living abroad") +
  theme_light()
## Warning: Removed 8023 rows containing missing values (geom_point).

2016

ggplot(euro2016,
       aes(x=DV, y=exptdmy))+
  geom_point(position = "jitter", alpha = 0.9)+
  #stat_summary(fun.y = mean, colour = "darkblue")+
  #stat_summary(fun.y = mean, colour = "darkgrey")+
  labs(x="Facilitating voting through e-voting", y = "Living Abroad") +
  theme_light()
## Warning: Removed 1181 rows containing missing values (geom_point).

2018

#"ELECTRONIC/ONLINE/MAIL VOTING CONCERNS - DIFFICULT TO USE"
ggplot(euro2018,
       aes(x=DV1, y=exptdmy))+
  geom_point(position="jitter", alpha=0.9)+
  #stat_summary(fun.y = mean, colour = "darkblue")+
  #stat_summary(fun.y = mean, colour = "darkgrey")+
  labs(x="Concerns about e-voting: Difficult to use", y = "Living Abroad") +
  theme_light()
## Warning: Removed 4138 rows containing missing values (geom_point).

# ELECTRONIC/ONLINE/MAIL VOTING CONCERNS - POTENTIAL FRAUD/CYBERATTACK"
ggplot(euro2018,
       aes(x=DV2, y=exptdmy))+
  geom_point(position="jitter", alpha=0.9)+
  #stat_summary(fun.y = mean, colour = "darkblue")+
  #stat_summary(fun.y = mean, colour = "darkgrey")+
  labs(x="Concerns about e-voting: Potential Fraud/Cyberattack", y = "Living Abroad") +
  theme_light()
## Warning: Removed 4266 rows containing missing values (geom_point).

Testing Correlations

After having recoded our DV and our IV, we are now able to model some causations, or assumed causations on how Europeans might think about evoting and what might affect this. Before modelling, we test for correlations.

2002

Tab02 <- CrossTable(euro2002$sex,euro2002$DV, prop.r = T, resid = T, digits = 3, chisq = T)
## 
##  
##    Cell Contents
## |-------------------------|
## |                       N |
## | Chi-square contribution |
## |           N / Row Total |
## |           N / Col Total |
## |         N / Table Total |
## |-------------------------|
## 
##  
## Total Observations in Table:  13350 
## 
##  
##              | euro2002$DV 
## euro2002$sex |         0 |         1 | Row Total | 
## -------------|-----------|-----------|-----------|
##            1 |      3308 |      3338 |      6646 | 
##              |     1.151 |     1.185 |           | 
##              |     0.498 |     0.502 |     0.498 | 
##              |     0.489 |     0.507 |           | 
##              |     0.248 |     0.250 |           | 
## -------------|-----------|-----------|-----------|
##            2 |      3462 |      3242 |      6704 | 
##              |     1.141 |     1.174 |           | 
##              |     0.516 |     0.484 |     0.502 | 
##              |     0.511 |     0.493 |           | 
##              |     0.259 |     0.243 |           | 
## -------------|-----------|-----------|-----------|
## Column Total |      6770 |      6580 |     13350 | 
##              |     0.507 |     0.493 |           | 
## -------------|-----------|-----------|-----------|
## 
##  
## Statistics for All Table Factors
## 
## 
## Pearson's Chi-squared test 
## ------------------------------------------------------------
## Chi^2 =  4.651813     d.f. =  1     p =  0.03102077 
## 
## Pearson's Chi-squared test with Yates' continuity correction 
## ------------------------------------------------------------
## Chi^2 =  4.577437     d.f. =  1     p =  0.03239564 
## 
## 
Tab02
## $t
##    y
## x      0    1
##   1 3308 3338
##   2 3462 3242
## 
## $prop.row
##    y
## x           0         1
##   1 0.4977430 0.5022570
##   2 0.5164081 0.4835919
## 
## $prop.col
##    y
## x           0         1
##   1 0.4886263 0.5072948
##   2 0.5113737 0.4927052
## 
## $prop.tbl
##    y
## x           0         1
##   1 0.2477903 0.2500375
##   2 0.2593258 0.2428464
## 
## $chisq
## 
##  Pearson's Chi-squared test
## 
## data:  t
## X-squared = 4.6518, df = 1, p-value = 0.03102
## 
## 
## $chisq.corr
## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  t
## X-squared = 4.5774, df = 1, p-value = 0.0324

2016

Tab16 <- CrossTable(euro2016$sex,euro2016$DV, prop.r = T, resid = T, digits = 3, chisq = T)
## 
##  
##    Cell Contents
## |-------------------------|
## |                       N |
## | Chi-square contribution |
## |           N / Row Total |
## |           N / Col Total |
## |         N / Table Total |
## |-------------------------|
## 
##  
## Total Observations in Table:  25868 
## 
##  
##              | euro2016$DV 
## euro2016$sex |         0 |         1 | Row Total | 
## -------------|-----------|-----------|-----------|
##            1 |      2762 |      8363 |     11125 | 
##              |     5.128 |     1.794 |           | 
##              |     0.248 |     0.752 |     0.430 | 
##              |     0.412 |     0.436 |           | 
##              |     0.107 |     0.323 |           | 
## -------------|-----------|-----------|-----------|
##            2 |      3943 |     10800 |     14743 | 
##              |     3.870 |     1.354 |           | 
##              |     0.267 |     0.733 |     0.570 | 
##              |     0.588 |     0.564 |           | 
##              |     0.152 |     0.418 |           | 
## -------------|-----------|-----------|-----------|
## Column Total |      6705 |     19163 |     25868 | 
##              |     0.259 |     0.741 |           | 
## -------------|-----------|-----------|-----------|
## 
##  
## Statistics for All Table Factors
## 
## 
## Pearson's Chi-squared test 
## ------------------------------------------------------------
## Chi^2 =  12.14652     d.f. =  1     p =  0.0004917961 
## 
## Pearson's Chi-squared test with Yates' continuity correction 
## ------------------------------------------------------------
## Chi^2 =  12.04684     d.f. =  1     p =  0.0005188029 
## 
## 
Tab16
## $t
##    y
## x       0     1
##   1  2762  8363
##   2  3943 10800
## 
## $prop.row
##    y
## x           0         1
##   1 0.2482697 0.7517303
##   2 0.2674490 0.7325510
## 
## $prop.col
##    y
## x           0         1
##   1 0.4119314 0.4364139
##   2 0.5880686 0.5635861
## 
## $prop.tbl
##    y
## x           0         1
##   1 0.1067728 0.3232952
##   2 0.1524277 0.4175043
## 
## $chisq
## 
##  Pearson's Chi-squared test
## 
## data:  t
## X-squared = 12.147, df = 1, p-value = 0.0004918
## 
## 
## $chisq.corr
## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  t
## X-squared = 12.047, df = 1, p-value = 0.0005188

2018

Tab18 <- CrossTable(euro2018$sex, euro2018$DV1, prop.r = T, resid = T, digits = 3, chisq = T)
## 
##  
##    Cell Contents
## |-------------------------|
## |                       N |
## | Chi-square contribution |
## |           N / Row Total |
## |           N / Col Total |
## |         N / Table Total |
## |-------------------------|
## 
##  
## Total Observations in Table:  26247 
## 
##  
##              | euro2018$DV1 
## euro2018$sex |         0 |         1 | Row Total | 
## -------------|-----------|-----------|-----------|
##            1 |      7623 |      4275 |     11898 | 
##              |    11.934 |    23.855 |           | 
##              |     0.641 |     0.359 |     0.453 | 
##              |     0.436 |     0.488 |           | 
##              |     0.290 |     0.163 |           | 
## -------------|-----------|-----------|-----------|
##            2 |      9872 |      4477 |     14349 | 
##              |     9.895 |    19.780 |           | 
##              |     0.688 |     0.312 |     0.547 | 
##              |     0.564 |     0.512 |           | 
##              |     0.376 |     0.171 |           | 
## -------------|-----------|-----------|-----------|
## Column Total |     17495 |      8752 |     26247 | 
##              |     0.667 |     0.333 |           | 
## -------------|-----------|-----------|-----------|
## 
##  
## Statistics for All Table Factors
## 
## 
## Pearson's Chi-squared test 
## ------------------------------------------------------------
## Chi^2 =  65.46482     d.f. =  1     p =  5.916071e-16 
## 
## Pearson's Chi-squared test with Yates' continuity correction 
## ------------------------------------------------------------
## Chi^2 =  65.25219     d.f. =  1     p =  6.590075e-16 
## 
## 
Tab18
## $t
##    y
## x      0    1
##   1 7623 4275
##   2 9872 4477
## 
## $prop.row
##    y
## x           0         1
##   1 0.6406959 0.3593041
##   2 0.6879922 0.3120078
## 
## $prop.col
##    y
## x           0         1
##   1 0.4357245 0.4884598
##   2 0.5642755 0.5115402
## 
## $prop.tbl
##    y
## x           0         1
##   1 0.2904332 0.1628758
##   2 0.3761192 0.1705719
## 
## $chisq
## 
##  Pearson's Chi-squared test
## 
## data:  t
## X-squared = 65.465, df = 1, p-value = 5.916e-16
## 
## 
## $chisq.corr
## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  t
## X-squared = 65.252, df = 1, p-value = 6.59e-16
Tab18_2 <- CrossTable(euro2018$sex, euro2018$DV2, prop.r = T, resid = T, digits = 3, chisq = T)
## 
##  
##    Cell Contents
## |-------------------------|
## |                       N |
## | Chi-square contribution |
## |           N / Row Total |
## |           N / Col Total |
## |         N / Table Total |
## |-------------------------|
## 
##  
## Total Observations in Table:  26005 
## 
##  
##              | euro2018$DV2 
## euro2018$sex |         0 |         1 | Row Total | 
## -------------|-----------|-----------|-----------|
##            1 |      7931 |      3935 |     11866 | 
##              |     1.088 |     2.273 |           | 
##              |     0.668 |     0.332 |     0.456 | 
##              |     0.451 |     0.467 |           | 
##              |     0.305 |     0.151 |           | 
## -------------|-----------|-----------|-----------|
##            2 |      9655 |      4484 |     14139 | 
##              |     0.913 |     1.907 |           | 
##              |     0.683 |     0.317 |     0.544 | 
##              |     0.549 |     0.533 |           | 
##              |     0.371 |     0.172 |           | 
## -------------|-----------|-----------|-----------|
## Column Total |     17586 |      8419 |     26005 | 
##              |     0.676 |     0.324 |           | 
## -------------|-----------|-----------|-----------|
## 
##  
## Statistics for All Table Factors
## 
## 
## Pearson's Chi-squared test 
## ------------------------------------------------------------
## Chi^2 =  6.180944     d.f. =  1     p =  0.01291334 
## 
## Pearson's Chi-squared test with Yates' continuity correction 
## ------------------------------------------------------------
## Chi^2 =  6.11497     d.f. =  1     p =  0.01340417 
## 
## 
Tab18_2
## $t
##    y
## x      0    1
##   1 7931 3935
##   2 9655 4484
## 
## $prop.row
##    y
## x           0         1
##   1 0.6683802 0.3316198
##   2 0.6828630 0.3171370
## 
## $prop.col
##    y
## x           0         1
##   1 0.4509837 0.4673952
##   2 0.5490163 0.5326048
## 
## $prop.tbl
##    y
## x           0         1
##   1 0.3049798 0.1513171
##   2 0.3712748 0.1724284
## 
## $chisq
## 
##  Pearson's Chi-squared test
## 
## data:  t
## X-squared = 6.1809, df = 1, p-value = 0.01291
## 
## 
## $chisq.corr
## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  t
## X-squared = 6.115, df = 1, p-value = 0.0134

Models

2002

First, we run a model with the technical affinity of respondents as DV, this model is a linear one, since this DV is of metric scale. The second model needs to be a log reg model since this DV is a dummy variable.

lm1_02 <- lm(newtech ~ tech + effdmy + exptdmy + evotedmy + country + sex + age + educ,
            data = euro2002,
            #family = binomial(link = "logit"),
            weights = wght_all)
summary(lm1_02)
## 
## Call:
## lm(formula = newtech ~ tech + effdmy + exptdmy + evotedmy + country + 
##     sex + age + educ, data = euro2002, weights = wght_all)
## 
## Weighted Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.60545 -0.37705 -0.02727  0.32166  2.04402 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           3.5889076  0.0548819  65.393  < 2e-16 ***
## tech                  0.0990827  0.0245199   4.041 5.39e-05 ***
## effdmy               -1.3006965  0.0145563 -89.356  < 2e-16 ***
## exptdmy              -0.0037031  0.0130693  -0.283 0.776920    
## evotedmy             -1.0019180  0.0143098 -70.016  < 2e-16 ***
## countryDenmark       -0.2390086  0.0335707  -7.120 1.20e-12 ***
## countryGermany       -0.1747271  0.0319043  -5.477 4.50e-08 ***
## countryGreece        -0.1358161  0.0353521  -3.842 0.000123 ***
## countrySpain         -0.1963848  0.0421017  -4.665 3.15e-06 ***
## countryFrance         0.0401580  0.0348134   1.154 0.248739    
## countryIreland       -0.1424716  0.0366678  -3.885 0.000103 ***
## countryItaly         -0.0854271  0.0349623  -2.443 0.014575 *  
## countryLuxembourg    -0.2098782  0.0415438  -5.052 4.49e-07 ***
## countryNetherlands    0.0288501  0.0380749   0.758 0.448647    
## countryPortugal      -0.1015236  0.0396301  -2.562 0.010436 *  
## countryUnited Kindom -0.0721911  0.0341781  -2.112 0.034707 *  
## countryAustria       -0.1806068  0.0350599  -5.151 2.66e-07 ***
## countrySweden        -0.0817162  0.0349454  -2.338 0.019396 *  
## countryFinland       -0.1746093  0.0335723  -5.201 2.04e-07 ***
## sex                   0.0477494  0.0125179   3.814 0.000138 ***
## age                   0.0041775  0.0004221   9.897  < 2e-16 ***
## educ                 -0.0001710  0.0002821  -0.606 0.544509    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.507 on 6597 degrees of freedom
##   (10166 observations deleted due to missingness)
## Multiple R-squared:  0.7507, Adjusted R-squared:  0.7499 
## F-statistic: 945.9 on 21 and 6597 DF,  p-value: < 2.2e-16
lm2_02 <- lm(exptdmy ~ tech + effdmy + newtech + evotedmy + country + sex + age + educ,
            data = euro2002,
            #family = binomial(link = "logit"),
            weights = wght_all)
summary(lm2_02)
## 
## Call:
## lm(formula = exptdmy ~ tech + effdmy + newtech + evotedmy + country + 
##     sex + age + educ, data = euro2002, weights = wght_all)
## 
## Weighted Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.1482 -0.3842 -0.2481  0.4783  1.2097 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           0.2897243  0.0662794   4.371 1.25e-05 ***
## tech                  0.0118027  0.0231269   0.510  0.60983    
## effdmy                0.0025378  0.0203868   0.124  0.90094    
## newtech              -0.0032863  0.0115983  -0.283  0.77692    
## evotedmy             -0.0687968  0.0177776  -3.870  0.00011 ***
## countryDenmark        0.2549385  0.0315907   8.070 8.28e-16 ***
## countryGermany       -0.0300843  0.0301212  -0.999  0.31794    
## countryGreece        -0.1385762  0.0332968  -4.162 3.20e-05 ***
## countrySpain         -0.0962987  0.0397093  -2.425  0.01533 *  
## countryFrance         0.0553512  0.0327920   1.688  0.09147 .  
## countryIreland        0.0306522  0.0345800   0.886  0.37543    
## countryItaly         -0.0420344  0.0329468  -1.276  0.20206    
## countryLuxembourg     0.0625133  0.0392041   1.595  0.11086    
## countryNetherlands    0.0728614  0.0358586   2.032  0.04220 *  
## countryPortugal      -0.0467479  0.0373474  -1.252  0.21072    
## countryUnited Kindom  0.0322090  0.0322057   1.000  0.31730    
## countryAustria        0.1951539  0.0330069   5.913 3.54e-09 ***
## countrySweden         0.1959845  0.0328452   5.967 2.54e-09 ***
## countryFinland        0.3040810  0.0314694   9.663  < 2e-16 ***
## sex                   0.0175191  0.0118035   1.484  0.13779    
## age                   0.0009048  0.0004004   2.260  0.02387 *  
## educ                  0.0004370  0.0002657   1.645  0.10009    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4776 on 6597 degrees of freedom
##   (10166 observations deleted due to missingness)
## Multiple R-squared:  0.07973,    Adjusted R-squared:  0.0768 
## F-statistic: 27.22 on 21 and 6597 DF,  p-value: < 2.2e-16
# we now change the Lm-DV with evotedmy and test glms
glm02_NULL <- glm(DV~1,
                 data = euro2002,
                 family = binomial(link = "logit"),
                 weights = wght_all)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
glm1_02 <- glm(DV ~ tech + effdmy + newtech + exptdmy + isocntry + sex + age + educ,  
            data = euro2002,
            family = binomial(link = "logit"),
            weights = wght_all)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
summary(glm1_02)
## 
## Call:
## glm(formula = DV ~ tech + effdmy + newtech + exptdmy + isocntry + 
##     sex + age + educ, family = binomial(link = "logit"), data = euro2002, 
##     weights = wght_all)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.5916  -0.3460  -0.0753   0.1604   5.7637  
## 
## Coefficients:
##                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)    13.501538   0.532861  25.338  < 2e-16 ***
## tech            0.322548   0.178823   1.804 0.071275 .  
## effdmy         -4.237194   0.173184 -24.466  < 2e-16 ***
## newtech        -5.219904   0.143426 -36.394  < 2e-16 ***
## exptdmy        -0.207369   0.087993  -2.357 0.018440 *  
## isocntryBE      1.337887   0.253619   5.275 1.33e-07 ***
## isocntryDE-E    0.770445   0.256904   2.999 0.002709 ** 
## isocntryDE-W    0.689473   0.242867   2.839 0.004527 ** 
## isocntryDK     -1.020915   0.235067  -4.343 1.40e-05 ***
## isocntryES      0.920850   0.265877   3.463 0.000533 ***
## isocntryFI     -0.165552   0.226890  -0.730 0.465599    
## isocntryFR      0.967305   0.252977   3.824 0.000131 ***
## isocntryGB-GBN  0.945752   0.240520   3.932 8.42e-05 ***
## isocntryGB-NIR  0.341533   0.322301   1.060 0.289295    
## isocntryGR     -0.342037   0.256854  -1.332 0.182980    
## isocntryIE      0.533520   0.244675   2.181 0.029219 *  
## isocntryIT      1.257448   0.242733   5.180 2.21e-07 ***
## isocntryLU      0.396020   0.260617   1.520 0.128625    
## isocntryNL      2.497709   0.249393  10.015  < 2e-16 ***
## isocntryNO      0.593393   0.236768   2.506 0.012203 *  
## isocntryPT      1.162165   0.257100   4.520 6.18e-06 ***
## isocntrySE      1.089948   0.235521   4.628 3.70e-06 ***
## sex             0.262650   0.084420   3.111 0.001863 ** 
## age             0.002931   0.002880   1.018 0.308795    
## educ           -0.001844   0.001756  -1.051 0.293460    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 8932.9  on 7044  degrees of freedom
## Residual deviance: 3646.0  on 7020  degrees of freedom
##   (9740 observations deleted due to missingness)
## AIC: 3854.6
## 
## Number of Fisher Scoring iterations: 7
glm2_02 <- glm(exptdmy ~ tech + effdmy + newtech + evotedmy + isocntry + sex + age + educ,  
            data = euro2002,
            family = binomial(link = "logit"),
            weights = wght_all)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
summary(glm2_02)
## 
## Call:
## glm(formula = exptdmy ~ tech + effdmy + newtech + evotedmy + 
##     isocntry + sex + age + educ, family = binomial(link = "logit"), 
##     data = euro2002, weights = wght_all)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.6150  -1.0001  -0.7023   1.1169   2.6574  
## 
## Coefficients:
##                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)    -0.209308   0.273464  -0.765 0.444035    
## tech            0.055875   0.100861   0.554 0.579589    
## effdmy          0.030430   0.086866   0.350 0.726105    
## newtech        -0.002520   0.049651  -0.051 0.959527    
## evotedmy       -0.277737   0.075578  -3.675 0.000238 ***
## isocntryBE     -0.828152   0.140419  -5.898 3.69e-09 ***
## isocntryDE-E   -0.676746   0.143364  -4.720 2.35e-06 ***
## isocntryDE-W   -1.211069   0.152336  -7.950 1.87e-15 ***
## isocntryDK      0.259891   0.127460   2.039 0.041449 *  
## isocntryES     -1.244195   0.178547  -6.968 3.20e-12 ***
## isocntryFI      0.477549   0.129559   3.686 0.000228 ***
## isocntryFR     -0.536962   0.134961  -3.979 6.93e-05 ***
## isocntryGB-GBN -0.716732   0.140651  -5.096 3.47e-07 ***
## isocntryGB-NIR -0.680450   0.190846  -3.565 0.000363 ***
## isocntryGR     -1.459541   0.146477  -9.964  < 2e-16 ***
## isocntryIE     -0.617407   0.144628  -4.269 1.96e-05 ***
## isocntryIT     -0.983653   0.140130  -7.020 2.23e-12 ***
## isocntryLU     -0.576197   0.147763  -3.899 9.64e-05 ***
## isocntryNL     -0.492473   0.148444  -3.318 0.000908 ***
## isocntryNO     -0.004898   0.136930  -0.036 0.971468    
## isocntryPT     -1.015202   0.164031  -6.189 6.05e-10 ***
## isocntrySE      0.009564   0.132595   0.072 0.942496    
## sex             0.072858   0.050210   1.451 0.146759    
## age             0.005097   0.001704   2.991 0.002781 ** 
## educ            0.001571   0.001126   1.396 0.162750    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 9708.5  on 7044  degrees of freedom
## Residual deviance: 9127.8  on 7020  degrees of freedom
##   (9740 observations deleted due to missingness)
## AIC: 9575.2
## 
## Number of Fisher Scoring iterations: 4

2016

We do the exact same models for 2016.

lm1_16 <- lm(exptdmy ~ tech + evotedmy + isocntry + sex + age + educ,
             data = euro2016,
             #family = binomial(link = "logit"),
             )
summary(lm1_16)
## 
## Call:
## lm(formula = exptdmy ~ tech + evotedmy + isocntry + sex + age + 
##     educ, data = euro2016)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.44644 -0.29640 -0.17503  0.04763  1.10455 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.0420991  0.0205678  -2.047 0.040683 *  
## tech         0.0038277  0.0035447   1.080 0.280218    
## evotedmy     0.2492808  0.0061339  40.640  < 2e-16 ***
## isocntryBE  -0.0224268  0.0185326  -1.210 0.226242    
## isocntryBG  -0.1355911  0.0185670  -7.303 2.90e-13 ***
## isocntryCY  -0.0480918  0.0228234  -2.107 0.035116 *  
## isocntryCZ   0.0136076  0.0187112   0.727 0.467084    
## isocntryDE  -0.0173552  0.0187947  -0.923 0.355804    
## isocntryDK  -0.0885941  0.0187407  -4.727 2.29e-06 ***
## isocntryEE  -0.1252799  0.0189748  -6.602 4.13e-11 ***
## isocntryES  -0.0755409  0.0186165  -4.058 4.97e-05 ***
## isocntryFI   0.0135479  0.0192170   0.705 0.480819    
## isocntryFR  -0.0130874  0.0185325  -0.706 0.480077    
## isocntryGB  -0.0377554  0.0187739  -2.011 0.044330 *  
## isocntryGR  -0.1314848  0.0186306  -7.057 1.74e-12 ***
## isocntryHR  -0.0145501  0.0188590  -0.772 0.440405    
## isocntryHU  -0.0300761  0.0185994  -1.617 0.105880    
## isocntryIE  -0.0821937  0.0186276  -4.412 1.03e-05 ***
## isocntryIT  -0.0196352  0.0185756  -1.057 0.290502    
## isocntryLT  -0.0795258  0.0187043  -4.252 2.13e-05 ***
## isocntryLU  -0.0033026  0.0227235  -0.145 0.884445    
## isocntryLV  -0.0141364  0.0186972  -0.756 0.449614    
## isocntryMT  -0.1854538  0.0231374  -8.015 1.15e-15 ***
## isocntryNL  -0.0395373  0.0187139  -2.113 0.034635 *  
## isocntryPL  -0.0193818  0.0186042  -1.042 0.297515    
## isocntryPT  -0.0989108  0.0187559  -5.274 1.35e-07 ***
## isocntryRO  -0.1650600  0.0187015  -8.826  < 2e-16 ***
## isocntrySE  -0.1303046  0.0187132  -6.963 3.41e-12 ***
## isocntrySI  -0.0666218  0.0186349  -3.575 0.000351 ***
## isocntrySK  -0.0078949  0.0188499  -0.419 0.675344    
## sex          0.0292406  0.0052182   5.604 2.12e-08 ***
## age          0.0016645  0.0001715   9.708  < 2e-16 ***
## educ        -0.0001686  0.0001430  -1.179 0.238602    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4082 on 25343 degrees of freedom
##   (1181 observations deleted due to missingness)
## Multiple R-squared:  0.07649,    Adjusted R-squared:  0.07532 
## F-statistic: 65.59 on 32 and 25343 DF,  p-value: < 2.2e-16
# now with glm
glm16_NULL <- glm(DV~1,
                 data = euro2016,
                 family = binomial(link = "logit"))

glm1_16 <- glm(DV ~ tech + exptdmy + isocntry + sex + age + educ,
             data = euro2016,
             family = binomial(link = "logit"))
summary(glm1_16)
## 
## Call:
## glm(formula = DV ~ tech + exptdmy + isocntry + sex + age + educ, 
##     family = binomial(link = "logit"), data = euro2016)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.9647  -0.8677   0.5104   0.7819   1.7886  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  2.0170017  0.1180262  17.089  < 2e-16 ***
## tech         0.1179965  0.0221164   5.335 9.54e-08 ***
## exptdmy      1.9071079  0.0538882  35.390  < 2e-16 ***
## isocntryBE   0.7542907  0.1061731   7.104 1.21e-12 ***
## isocntryBG   1.4025596  0.1110235  12.633  < 2e-16 ***
## isocntryCY   0.8992912  0.1365626   6.585 4.54e-11 ***
## isocntryCZ   1.1336872  0.1127942  10.051  < 2e-16 ***
## isocntryDE   0.1275481  0.1020883   1.249 0.211523    
## isocntryDK   1.5584029  0.1152261  13.525  < 2e-16 ***
## isocntryEE   1.5113349  0.1161178  13.016  < 2e-16 ***
## isocntryES   0.7041404  0.1061682   6.632 3.30e-11 ***
## isocntryFI   1.7092578  0.1232460  13.869  < 2e-16 ***
## isocntryFR   0.0601246  0.1006354   0.597 0.550207    
## isocntryGB   0.9884966  0.1096911   9.012  < 2e-16 ***
## isocntryGR   0.6532978  0.1044482   6.255 3.98e-10 ***
## isocntryHR   0.4082647  0.1048936   3.892 9.93e-05 ***
## isocntryHU   0.1101176  0.1006486   1.094 0.273920    
## isocntryIE   1.0065049  0.1064497   9.455  < 2e-16 ***
## isocntryIT   0.1835950  0.1016132   1.807 0.070793 .  
## isocntryLT   1.4905485  0.1168129  12.760  < 2e-16 ***
## isocntryLU   0.1689104  0.1254155   1.347 0.178043    
## isocntryLV   1.2071860  0.1128462  10.698  < 2e-16 ***
## isocntryMT   0.9436077  0.1331068   7.089 1.35e-12 ***
## isocntryNL   1.0826233  0.1106143   9.787  < 2e-16 ***
## isocntryPL   0.8745980  0.1076405   8.125 4.47e-16 ***
## isocntryPT   1.2349895  0.1151379  10.726  < 2e-16 ***
## isocntryRO   0.7941865  0.1052356   7.547 4.46e-14 ***
## isocntrySE   1.5847699  0.1149029  13.792  < 2e-16 ***
## isocntrySI   0.2964433  0.1010702   2.933 0.003357 ** 
## isocntrySK   1.1906099  0.1155949  10.300  < 2e-16 ***
## sex         -0.0863878  0.0318827  -2.710 0.006737 ** 
## age         -0.0351728  0.0010675 -32.948  < 2e-16 ***
## educ        -0.0032282  0.0009389  -3.438 0.000585 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 28869  on 25375  degrees of freedom
## Residual deviance: 24879  on 25343  degrees of freedom
##   (1181 observations deleted due to missingness)
## AIC: 24945
## 
## Number of Fisher Scoring iterations: 5
glm2_16 <- glm(exptdmy ~ tech + evotedmy + isocntry + sex + age + educ,
             data = euro2016,
             family = binomial(link = "logit"))
summary(glm2_16)
## 
## Call:
## glm(formula = exptdmy ~ tech + evotedmy + isocntry + sex + age + 
##     educ, family = binomial(link = "logit"), data = euro2016)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.1831  -0.8335  -0.5699  -0.2458   2.8041  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -3.3338034  0.1316591 -25.321  < 2e-16 ***
## tech         0.0204433  0.0214639   0.952 0.340868    
## evotedmy     1.9275475  0.0538953  35.765  < 2e-16 ***
## isocntryBE  -0.1555836  0.1086396  -1.432 0.152113    
## isocntryBG  -0.8582913  0.1161425  -7.390 1.47e-13 ***
## isocntryCY  -0.3038437  0.1343468  -2.262 0.023720 *  
## isocntryCZ   0.0200143  0.1072232   0.187 0.851927    
## isocntryDE  -0.1081111  0.1128309  -0.958 0.337977    
## isocntryDK  -0.5326685  0.1118685  -4.762 1.92e-06 ***
## isocntryEE  -0.7593140  0.1167754  -6.502 7.91e-11 ***
## isocntryES  -0.4697832  0.1132799  -4.147 3.37e-05 ***
## isocntryFI   0.0051875  0.1092189   0.047 0.962118    
## isocntryFR  -0.0851042  0.1112240  -0.765 0.444176    
## isocntryGB  -0.2411262  0.1100406  -2.191 0.028434 *  
## isocntryGR  -0.8815353  0.1210523  -7.282 3.28e-13 ***
## isocntryHR  -0.1063471  0.1114345  -0.954 0.339908    
## isocntryHU  -0.1870784  0.1129810  -1.656 0.097754 .  
## isocntryIE  -0.5084315  0.1125006  -4.519 6.20e-06 ***
## isocntryIT  -0.1263592  0.1112632  -1.136 0.256091    
## isocntryLT  -0.4746908  0.1112190  -4.268 1.97e-05 ***
## isocntryLU  -0.0327620  0.1341583  -0.244 0.807073    
## isocntryLV  -0.1248662  0.1079239  -1.157 0.247279    
## isocntryMT  -1.3171700  0.1693955  -7.776 7.50e-15 ***
## isocntryNL  -0.2612266  0.1094163  -2.387 0.016965 *  
## isocntryPL  -0.1475759  0.1085949  -1.359 0.174160    
## isocntryPT  -0.6058853  0.1139005  -5.319 1.04e-07 ***
## isocntryRO  -1.2018720  0.1305990  -9.203  < 2e-16 ***
## isocntrySE  -0.7915289  0.1153849  -6.860 6.89e-12 ***
## isocntrySI  -0.4117450  0.1152369  -3.573 0.000353 ***
## isocntrySK  -0.0925293  0.1083561  -0.854 0.393140    
## sex          0.1842363  0.0315358   5.842 5.15e-09 ***
## age          0.0100923  0.0010397   9.707  < 2e-16 ***
## educ        -0.0011454  0.0009031  -1.268 0.204655    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 27723  on 25375  degrees of freedom
## Residual deviance: 25443  on 25343  degrees of freedom
##   (1181 observations deleted due to missingness)
## AIC: 25509
## 
## Number of Fisher Scoring iterations: 5

2018

We do the same models for 2018.

lm1_18 <- lm(exptdmy ~ tech + evotedmy + netuse + isocntry + sex + age + educ,
            data = euro2018,
            #family = binomial(link = "logit"),
            )
summary(lm1_18)
## 
## Call:
## lm(formula = exptdmy ~ tech + evotedmy + netuse + isocntry + 
##     sex + age + educ, data = euro2018)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.00218 -0.44979  0.08658  0.38314  1.14577 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.7598755  0.0212766  35.714  < 2e-16 ***
## tech          0.0042640  0.0034447   1.238  0.21578    
## evotedmy      0.0868726  0.0063613  13.657  < 2e-16 ***
## netuse       -0.0727541  0.0018442 -39.450  < 2e-16 ***
## isocntryBE    0.1853696  0.0211421   8.768  < 2e-16 ***
## isocntryBG    0.0389777  0.0219941   1.772  0.07638 .  
## isocntryCY    0.1163781  0.0264173   4.405 1.06e-05 ***
## isocntryCZ    0.1056411  0.0213788   4.941 7.81e-07 ***
## isocntryDE-E -0.0032034  0.0274168  -0.117  0.90699    
## isocntryDE-W -0.0690697  0.0218583  -3.160  0.00158 ** 
## isocntryDK    0.1991319  0.0211537   9.414  < 2e-16 ***
## isocntryEE    0.2893721  0.0219972  13.155  < 2e-16 ***
## isocntryES    0.0585759  0.0213406   2.745  0.00606 ** 
## isocntryFI    0.2183475  0.0210606  10.368  < 2e-16 ***
## isocntryFR    0.0964822  0.0211882   4.554 5.30e-06 ***
## isocntryGB    0.1846985  0.0213373   8.656  < 2e-16 ***
## isocntryGR   -0.0213754  0.0215461  -0.992  0.32117    
## isocntryHR    0.1586507  0.0211167   7.513 5.98e-14 ***
## isocntryHU   -0.0616962  0.0211055  -2.923  0.00347 ** 
## isocntryIE    0.1357823  0.0211161   6.430 1.30e-10 ***
## isocntryIT   -0.0329923  0.0213598  -1.545  0.12246    
## isocntryLT    0.2090743  0.0218095   9.586  < 2e-16 ***
## isocntryLU    0.0381239  0.0266141   1.432  0.15202    
## isocntryLV    0.2319389  0.0212960  10.891  < 2e-16 ***
## isocntryMT    0.1691197  0.0265245   6.376 1.85e-10 ***
## isocntryNL    0.3309158  0.0209943  15.762  < 2e-16 ***
## isocntryPL    0.1308195  0.0214599   6.096 1.10e-09 ***
## isocntryPT    0.0077316  0.0218590   0.354  0.72356    
## isocntryRO    0.0089491  0.0214482   0.417  0.67651    
## isocntrySE    0.0557181  0.0208861   2.668  0.00764 ** 
## isocntrySI    0.0641573  0.0218889   2.931  0.00338 ** 
## isocntrySK    0.1396380  0.0223824   6.239 4.49e-10 ***
## sex          -0.0068868  0.0058850  -1.170  0.24192    
## age          -0.0044715  0.0002159 -20.714  < 2e-16 ***
## educ         -0.0001714  0.0001435  -1.194  0.23255    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4451 on 23301 degrees of freedom
##   (4138 observations deleted due to missingness)
## Multiple R-squared:  0.2086, Adjusted R-squared:  0.2075 
## F-statistic: 180.7 on 34 and 23301 DF,  p-value: < 2.2e-16
glm18_NULL1 <- glm(DV1~1,
                 data = euro2018,
                 family = binomial(link = "logit"))
glm18_NULL2 <- glm(DV2~1,
                 data = euro2018,
                 family = binomial(link = "logit"))

glm1_18 <- glm(DV1 ~ tech + exptdmy + netuse + isocntry + sex + age + educ, # adding variable on New technologies will create more possibilities for expressing your opinion
            data = euro2018,
            family = binomial(link = "logit"))
summary(glm1_18)
## 
## Call:
## glm(formula = DV1 ~ tech + exptdmy + netuse + isocntry + sex + 
##     age + educ, family = binomial(link = "logit"), data = euro2018)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.5608  -0.8942  -0.7371   1.2758   2.1064  
## 
## Coefficients:
##                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -0.2095320  0.1043202  -2.009 0.044585 *  
## tech         -0.0164819  0.0170046  -0.969 0.332414    
## exptdmy       0.4394135  0.0324143  13.556  < 2e-16 ***
## netuse        0.0288821  0.0095383   3.028 0.002462 ** 
## isocntryBE   -0.3281828  0.1015380  -3.232 0.001229 ** 
## isocntryBG   -0.5351769  0.1090220  -4.909 9.16e-07 ***
## isocntryCY   -0.6037614  0.1340177  -4.505 6.63e-06 ***
## isocntryCZ    0.1938971  0.0993040   1.953 0.050872 .  
## isocntryDE-E  0.9613524  0.1270535   7.567 3.83e-14 ***
## isocntryDE-W  0.6298342  0.1009702   6.238 4.44e-10 ***
## isocntryDK   -0.3815754  0.1024343  -3.725 0.000195 ***
## isocntryEE    0.2280763  0.1021451   2.233 0.025558 *  
## isocntryES   -1.1244597  0.1168770  -9.621  < 2e-16 ***
## isocntryFI   -0.6007393  0.1043533  -5.757 8.57e-09 ***
## isocntryFR   -0.6659235  0.1066055  -6.247 4.19e-10 ***
## isocntryGB   -0.5466351  0.1051148  -5.200 1.99e-07 ***
## isocntryGR   -0.5866459  0.1076823  -5.448 5.10e-08 ***
## isocntryHR   -0.1508771  0.0998526  -1.511 0.130789    
## isocntryHU   -0.1121974  0.1005561  -1.116 0.264521    
## isocntryIE   -0.9077672  0.1097377  -8.272  < 2e-16 ***
## isocntryIT   -0.5569713  0.1064168  -5.234 1.66e-07 ***
## isocntryLT   -0.2934655  0.1049811  -2.795 0.005183 ** 
## isocntryLU   -0.3166605  0.1297589  -2.440 0.014672 *  
## isocntryLV   -0.4631526  0.1037956  -4.462 8.11e-06 ***
## isocntryMT   -0.5275667  0.1335841  -3.949 7.84e-05 ***
## isocntryNL   -0.1790010  0.0997415  -1.795 0.072709 .  
## isocntryPL    0.0700961  0.1003249   0.699 0.484745    
## isocntryPT   -0.2481334  0.1051789  -2.359 0.018317 *  
## isocntryRO   -0.2718187  0.1028635  -2.643 0.008229 ** 
## isocntrySE    0.1782355  0.0974414   1.829 0.067376 .  
## isocntrySI    0.3779052  0.1011570   3.736 0.000187 ***
## isocntrySK    0.4094302  0.1032518   3.965 7.33e-05 ***
## sex          -0.2384590  0.0288843  -8.256  < 2e-16 ***
## age          -0.0035322  0.0010758  -3.283 0.001026 ** 
## educ         -0.0003376  0.0007073  -0.477 0.633184    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 29414  on 23335  degrees of freedom
## Residual deviance: 28225  on 23301  degrees of freedom
##   (4138 observations deleted due to missingness)
## AIC: 28295
## 
## Number of Fisher Scoring iterations: 4
glm2_18 <- glm(DV2 ~ tech + exptdmy + netuse + isocntry + sex + age + educ, # adding variable on New technologies will create more possibilities for expressing your opinion
            data = euro2018,
            family = binomial(link = "logit"))
summary(glm2_18)
## 
## Call:
## glm(formula = DV2 ~ tech + exptdmy + netuse + isocntry + sex + 
##     age + educ, family = binomial(link = "logit"), data = euro2018)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.3666  -0.9036  -0.7267   1.2973   2.0452  
## 
## Coefficients:
##                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -0.8231536  0.1054237  -7.808 5.81e-15 ***
## tech         -0.0055784  0.0171492  -0.325 0.744965    
## exptdmy       0.6176224  0.0329019  18.772  < 2e-16 ***
## netuse        0.0583611  0.0096155   6.069 1.28e-09 ***
## isocntryBE   -0.3494140  0.1022560  -3.417 0.000633 ***
## isocntryBG   -0.3817850  0.1079124  -3.538 0.000403 ***
## isocntryCY   -0.7886724  0.1403471  -5.619 1.92e-08 ***
## isocntryCZ   -0.2679601  0.1026621  -2.610 0.009051 ** 
## isocntryDE-E  0.2375602  0.1275794   1.862 0.062595 .  
## isocntryDE-W  0.0417748  0.1031801   0.405 0.685571    
## isocntryDK   -0.4431671  0.1033543  -4.288 1.80e-05 ***
## isocntryEE    0.3058517  0.1020733   2.996 0.002732 ** 
## isocntryES   -0.9383725  0.1131263  -8.295  < 2e-16 ***
## isocntryFI   -0.0593114  0.0999476  -0.593 0.552897    
## isocntryFR   -0.4198936  0.1036224  -4.052 5.07e-05 ***
## isocntryGB   -0.8790040  0.1102398  -7.974 1.54e-15 ***
## isocntryGR   -0.3662338  0.1060553  -3.453 0.000554 ***
## isocntryHR    0.0793642  0.0990829   0.801 0.423138    
## isocntryHU   -0.3938127  0.1039751  -3.788 0.000152 ***
## isocntryIE   -0.9544349  0.1111490  -8.587  < 2e-16 ***
## isocntryIT   -0.4057527  0.1053038  -3.853 0.000117 ***
## isocntryLT   -0.4395243  0.1068998  -4.112 3.93e-05 ***
## isocntryLU   -0.2461520  0.1290355  -1.908 0.056439 .  
## isocntryLV   -0.7256976  0.1072182  -6.768 1.30e-11 ***
## isocntryMT   -0.5831605  0.1359805  -4.289 1.80e-05 ***
## isocntryNL   -0.2432832  0.1004009  -2.423 0.015388 *  
## isocntryPL   -0.3097924  0.1035316  -2.992 0.002769 ** 
## isocntryPT   -0.2471129  0.1055956  -2.340 0.019274 *  
## isocntryRO   -0.4533012  0.1057127  -4.288 1.80e-05 ***
## isocntrySE    0.1876363  0.0977468   1.920 0.054906 .  
## isocntrySI    0.2568967  0.1018982   2.521 0.011698 *  
## isocntrySK    0.2622162  0.1042247   2.516 0.011874 *  
## sex          -0.0779459  0.0291176  -2.677 0.007430 ** 
## age           0.0005423  0.0010840   0.500 0.616883    
## educ         -0.0010050  0.0007172  -1.401 0.161148    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 28851  on 23207  degrees of freedom
## Residual deviance: 27883  on 23173  degrees of freedom
##   (4266 observations deleted due to missingness)
## AIC: 27953
## 
## Number of Fisher Scoring iterations: 4
glm3_18 <- glm(exptdmy ~ tech + evotedmy + netuse + isocntry + sex + age + educ, # adding variable on New technologies will create more possibilities for expressing your opinion
            data = euro2018,
            family = binomial(link = "logit"))
summary(glm3_18)
## 
## Call:
## glm(formula = exptdmy ~ tech + evotedmy + netuse + isocntry + 
##     sex + age + educ, family = binomial(link = "logit"), data = euro2018)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.2237  -1.0573  -0.2621   0.9608   2.6844  
## 
## Coefficients:
##                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   1.2726419  0.1053663  12.078  < 2e-16 ***
## tech          0.0267741  0.0176813   1.514 0.129959    
## evotedmy      0.4407661  0.0325487  13.542  < 2e-16 ***
## netuse       -0.4165696  0.0115565 -36.046  < 2e-16 ***
## isocntryBE    0.8726075  0.1043648   8.361  < 2e-16 ***
## isocntryBG    0.1782007  0.1087709   1.638 0.101357    
## isocntryCY    0.5772921  0.1328912   4.344 1.40e-05 ***
## isocntryCZ    0.5032679  0.1043445   4.823 1.41e-06 ***
## isocntryDE-E -0.0341546  0.1373609  -0.249 0.803632    
## isocntryDE-W -0.3796726  0.1104801  -3.437 0.000589 ***
## isocntryDK    0.9355203  0.1033637   9.051  < 2e-16 ***
## isocntryEE    1.4768563  0.1153724  12.801  < 2e-16 ***
## isocntryES    0.2794760  0.1047825   2.667 0.007649 ** 
## isocntryFI    1.0586066  0.1049014  10.091  < 2e-16 ***
## isocntryFR    0.4642693  0.1035182   4.485 7.29e-06 ***
## isocntryGB    0.8820839  0.1060148   8.320  < 2e-16 ***
## isocntryGR   -0.1500423  0.1081720  -1.387 0.165420    
## isocntryHR    0.7612831  0.1047652   7.267 3.69e-13 ***
## isocntryHU   -0.3508460  0.1077307  -3.257 0.001127 ** 
## isocntryIE    0.6452496  0.1029540   6.267 3.67e-10 ***
## isocntryIT   -0.1550258  0.1049985  -1.476 0.139821    
## isocntryLT    1.0817584  0.1117631   9.679  < 2e-16 ***
## isocntryLU    0.1928737  0.1279679   1.507 0.131758    
## isocntryLV    1.1468649  0.1073355  10.685  < 2e-16 ***
## isocntryMT    0.8408424  0.1359342   6.186 6.18e-10 ***
## isocntryNL    1.5960018  0.1094379  14.584  < 2e-16 ***
## isocntryPL    0.6591002  0.1071616   6.151 7.72e-10 ***
## isocntryPT    0.0068421  0.1094722   0.063 0.950164    
## isocntryRO    0.0488527  0.1073462   0.455 0.649041    
## isocntrySE    0.2821848  0.1006479   2.804 0.005052 ** 
## isocntrySI    0.3033754  0.1087366   2.790 0.005271 ** 
## isocntrySK    0.7239559  0.1127973   6.418 1.38e-10 ***
## sex          -0.0411675  0.0297993  -1.381 0.167127    
## age          -0.0211455  0.0010821 -19.542  < 2e-16 ***
## educ         -0.0010701  0.0007288  -1.468 0.141996    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 32349  on 23335  degrees of freedom
## Residual deviance: 26807  on 23301  degrees of freedom
##   (4138 observations deleted due to missingness)
## AIC: 26877
## 
## Number of Fisher Scoring iterations: 4
glmer2_18 <- glmer(DV2 ~ (1 | isocntry) +
                     tech + exptdmy + netuse + 
                     sex + age + educ,
                     data = euro2018,
                     family = binomial(link = "logit"),
                     control = glmerControl(optimizer = "bobyqa"),
                     nAGQ = 10)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?
summary(glmer2_18)
## Generalized linear mixed model fit by maximum likelihood (Adaptive
##   Gauss-Hermite Quadrature, nAGQ = 10) [glmerMod]
##  Family: binomial  ( logit )
## Formula: DV2 ~ (1 | isocntry) + tech + exptdmy + netuse + sex + age +      educ
##    Data: euro2018
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##  28014.9  28079.3 -13999.4  27998.9    23200 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.2252 -0.7092 -0.5525  1.1570  2.6133 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  isocntry (Intercept) 0.121    0.3479  
## Number of obs: 23208, groups:  isocntry, 29
## 
## Fixed effects:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.1169445  0.1058410 -10.553  < 2e-16 ***
## tech        -0.0067959  0.0169999  -0.400  0.68933    
## exptdmy      0.6165779  0.0328412  18.774  < 2e-16 ***
## netuse       0.0579993  0.0095907   6.047 1.47e-09 ***
## sex         -0.0779446  0.0290928  -2.679  0.00738 ** 
## age          0.0006121  0.0010803   0.567  0.57098    
## educ        -0.0009707  0.0007159  -1.356  0.17515    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##         (Intr) tech   exptdm netuse sex    age   
## tech    -0.174                                   
## exptdmy -0.296 -0.006                            
## netuse   0.025  0.036  0.276                     
## sex     -0.421 -0.006  0.007 -0.010              
## age     -0.482 -0.239  0.134 -0.454  0.000       
## educ    -0.352 -0.067  0.006 -0.062  0.010  0.363
## optimizer (bobyqa) convergence code: 0 (OK)
## Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?

Testing and Plotting Models

Here, we have a more deeper look into how our models are fitted to the baseline argument 1) that people more inclined towards efficacy measures and trust more in politics would opt more for e-voting than others and 2) that ex-pats would strongerly opt for e-voting than residents.

library("modelsummary")
## 
## Attaching package: 'modelsummary'
## The following object is masked from 'package:psych':
## 
##     SD
## The following object is masked from 'package:Hmisc':
## 
##     Mean
## The following objects are masked from 'package:DescTools':
## 
##     Format, Mean, Median, N, SD, Var
#first, we compare the linear models
modelsummary(list(lm2_02,lm1_16,lm1_18),
             vcov ="robust",
             group = term ~ model,
             statistic = "std.error",
             stars = TRUE)
Model 1 Model 2 Model 3
(Intercept) 0.290*** −0.042* 0.760***
(0.070) (0.020) (0.022)
tech 0.012 0.004 0.004
(0.024) (0.004) (0.003)
effdmy 0.003
(0.021)
newtech −0.003
(0.012)
evotedmy −0.069*** 0.249*** 0.087***
(0.019) (0.005) (0.006)
countryDenmark 0.255***
(0.033)
countryGermany −0.030
(0.031)
countryGreece −0.139***
(0.033)
countrySpain −0.096*
(0.039)
countryFrance 0.055
(0.035)
countryIreland 0.031
(0.038)
countryItaly −0.042
(0.034)
countryLuxembourg 0.063
(0.046)
countryNetherlands 0.073+
(0.038)
countryPortugal −0.047
(0.038)
countryUnited Kindom 0.032
(0.034)
countryAustria 0.195***
(0.036)
countrySweden 0.196***
(0.035)
countryFinland 0.304***
(0.033)
sex 0.018 0.029*** −0.007
(0.012) (0.005) (0.006)
age 0.001* 0.002*** −0.004***
(0.000) (0.000) (0.000)
educ 0.000 0.000 0.000
(0.000) (0.000) (0.000)
isocntryBE −0.022 0.185***
(0.019) (0.022)
isocntryBG −0.136*** 0.039+
(0.018) (0.022)
isocntryCY −0.048* 0.116***
(0.023) (0.027)
isocntryCZ 0.014 0.106***
(0.019) (0.022)
isocntryDE −0.017
(0.019)
isocntryDK −0.089*** 0.199***
(0.019) (0.022)
isocntryEE −0.125*** 0.289***
(0.018) (0.021)
isocntryES −0.076*** 0.059**
(0.018) (0.021)
isocntryFI 0.014 0.218***
(0.020) (0.021)
isocntryFR −0.013 0.096***
(0.018) (0.022)
isocntryGB −0.038* 0.185***
(0.019) (0.021)
isocntryGR −0.131*** −0.021
(0.017) (0.021)
isocntryHR −0.015 0.159***
(0.019) (0.021)
isocntryHU −0.030 −0.062**
(0.018) (0.021)
isocntryIE −0.082*** 0.136***
(0.018) (0.021)
isocntryIT −0.020 −0.033
(0.019) (0.022)
isocntryLT −0.080*** 0.209***
(0.019) (0.021)
isocntryLU −0.003 0.038
(0.023) (0.028)
isocntryLV −0.014 0.232***
(0.019) (0.021)
isocntryMT −0.185*** 0.169***
(0.020) (0.027)
isocntryNL −0.040* 0.331***
(0.019) (0.020)
isocntryPL −0.019 0.131***
(0.019) (0.022)
isocntryPT −0.099*** 0.008
(0.019) (0.022)
isocntryRO −0.165*** 0.009
(0.017) (0.023)
isocntrySE −0.130*** 0.056*
(0.018) (0.022)
isocntrySI −0.067*** 0.064**
(0.018) (0.022)
isocntrySK −0.008 0.140***
(0.020) (0.022)
netuse −0.073***
(0.002)
isocntryDE-E −0.003
(0.027)
isocntryDE-W −0.069**
(0.022)
Num.Obs. 6619 25376 23336
R2 0.080 0.076 0.209
R2 Adj. 0.077 0.075 0.207
AIC 9351.6 26580.0 28484.6
BIC 9508.0 26856.8 28774.6
RMSE 0.48 0.41 0.44
Std.Errors Robust Robust Robust
+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001 
# then we compare the 1st glms
modelsummary(list(glm1_02,glm1_16,glm1_18),
             vcov ="robust",
             group = term ~ model,
             statistic = "std.error",
             stars = TRUE)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Model 1 Model 2 Model 3
(Intercept) 13.502*** 2.017*** −0.210*
(0.594) (0.120) (0.104)
tech 0.323+ 0.118*** −0.016
(0.188) (0.022) (0.017)
effdmy −4.237***
(0.184)
newtech −5.220***
(0.166)
exptdmy −0.207* 1.907*** 0.439***
(0.093) (0.055) (0.032)
isocntryBE 1.338*** 0.754*** −0.328**
(0.256) (0.105) (0.101)
isocntryDE-E 0.770** 0.961***
(0.272) (0.128)
isocntryDE-W 0.689** 0.630***
(0.264) (0.101)
isocntryDK −1.021*** 1.558*** −0.382***
(0.236) (0.113) (0.102)
isocntryES 0.921*** 0.704*** −1.124***
(0.277) (0.106) (0.118)
isocntryFI −0.166 1.709*** −0.601***
(0.258) (0.122) (0.104)
isocntryFR 0.967*** 0.060 −0.666***
(0.267) (0.100) (0.107)
isocntryGB-GBN 0.946***
(0.253)
isocntryGB-NIR 0.342
(0.328)
isocntryGR −0.342 0.653*** −0.587***
(0.274) (0.106) (0.107)
isocntryIE 0.534+ 1.007*** −0.908***
(0.274) (0.107) (0.110)
isocntryIT 1.257*** 0.184+ −0.557***
(0.256) (0.102) (0.107)
isocntryLU 0.396 0.169 −0.317*
(0.312) (0.125) (0.132)
isocntryNL 2.498*** 1.083*** −0.179+
(0.247) (0.110) (0.100)
isocntryNO 0.593*
(0.261)
isocntryPT 1.162*** 1.235*** −0.248*
(0.257) (0.117) (0.106)
isocntrySE 1.090*** 1.585*** 0.178+
(0.255) (0.113) (0.097)
sex 0.263** −0.086** −0.238***
(0.089) (0.032) (0.029)
age 0.003 −0.035*** −0.004**
(0.003) (0.001) (0.001)
educ −0.002 −0.003** 0.000
(0.002) (0.001) (0.001)
isocntryBG 1.403*** −0.535***
(0.114) (0.109)
isocntryCY 0.899*** −0.604***
(0.138) (0.134)
isocntryCZ 1.134*** 0.194+
(0.112) (0.099)
isocntryDE 0.128
(0.101)
isocntryEE 1.511*** 0.228*
(0.113) (0.102)
isocntryGB 0.988*** −0.547***
(0.107) (0.105)
isocntryHR 0.408*** −0.151
(0.104) (0.099)
isocntryHU 0.110 −0.112
(0.102) (0.101)
isocntryLT 1.491*** −0.293**
(0.114) (0.105)
isocntryLV 1.207*** −0.463***
(0.112) (0.104)
isocntryMT 0.944*** −0.528***
(0.133) (0.133)
isocntryPL 0.875*** 0.070
(0.107) (0.100)
isocntryRO 0.794*** −0.272**
(0.107) (0.103)
isocntrySI 0.296** 0.378***
(0.100) (0.101)
isocntrySK 1.191*** 0.409***
(0.116) (0.104)
netuse 0.029**
(0.010)
Num.Obs. 7045 25376 23336
AIC 3854.6 24944.9 28294.6
BIC 4026.1 25213.6 28576.6
RMSE 0.28 0.40 0.46
Std.Errors Robust Robust Robust
+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001 
# then we compare the 2nd glms
modelsummary(list(glm2_02,glm2_16,glm2_18),
             vcov ="robust",
             group = term ~ model,
             statistic = "std.error",
             stars = TRUE)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Model 1 Model 2 Model 3
(Intercept) −0.209 −3.334*** −0.823***
(0.291) (0.132) (0.105)
tech 0.056 0.020 −0.006
(0.106) (0.022) (0.017)
effdmy 0.030
(0.092)
newtech −0.003
(0.052)
evotedmy −0.278*** 1.928***
(0.080) (0.055)
isocntryBE −0.828*** −0.156 −0.349***
(0.148) (0.107) (0.102)
isocntryDE-E −0.677*** 0.238+
(0.150) (0.127)
isocntryDE-W −1.211*** 0.042
(0.167) (0.104)
isocntryDK 0.260+ −0.533*** −0.443***
(0.138) (0.111) (0.103)
isocntryES −1.244*** −0.470*** −0.938***
(0.185) (0.112) (0.113)
isocntryFI 0.478*** 0.005 −0.059
(0.141) (0.108) (0.100)
isocntryFR −0.537*** −0.085 −0.420***
(0.145) (0.108) (0.103)
isocntryGB-GBN −0.717***
(0.151)
isocntryGB-NIR −0.680***
(0.200)
isocntryGR −1.460*** −0.882*** −0.366***
(0.157) (0.120) (0.106)
isocntryIE −0.617*** −0.508*** −0.954***
(0.160) (0.111) (0.111)
isocntryIT −0.984*** −0.126 −0.406***
(0.148) (0.111) (0.106)
isocntryLU −0.576*** −0.033 −0.246+
(0.170) (0.132) (0.130)
isocntryNL −0.492** −0.261* −0.243*
(0.157) (0.108) (0.100)
isocntryNO −0.005
(0.148)
isocntryPT −1.015*** −0.606*** −0.247*
(0.175) (0.113) (0.106)
isocntrySE 0.010 −0.792*** 0.188+
(0.144) (0.114) (0.097)
sex 0.073 0.184*** −0.078**
(0.053) (0.032) (0.029)
age 0.005** 0.010*** 0.001
(0.002) (0.001) (0.001)
educ 0.002 −0.001 −0.001
(0.001) (0.001) (0.001)
isocntryBG −0.858*** −0.382***
(0.116) (0.108)
isocntryCY −0.304* −0.789***
(0.133) (0.141)
isocntryCZ 0.020 −0.268**
(0.105) (0.103)
isocntryDE −0.108
(0.111)
isocntryEE −0.759*** 0.306**
(0.115) (0.101)
isocntryGB −0.241* −0.879***
(0.108) (0.111)
isocntryHR −0.106 0.079
(0.109) (0.098)
isocntryHU −0.187+ −0.394***
(0.113) (0.104)
isocntryLT −0.475*** −0.440***
(0.110) (0.107)
isocntryLV −0.125 −0.726***
(0.107) (0.107)
isocntryMT −1.317*** −0.583***
(0.170) (0.135)
isocntryPL −0.148 −0.310**
(0.107) (0.104)
isocntryRO −1.202*** −0.453***
(0.130) (0.106)
isocntrySI −0.412*** 0.257*
(0.114) (0.102)
isocntrySK −0.093 0.262*
(0.108) (0.105)
exptdmy 0.618***
(0.033)
netuse 0.058***
(0.010)
Num.Obs. 7045 25376 23208
AIC 9575.2 25509.3 27952.6
BIC 9746.7 25777.9 28234.4
RMSE 0.48 0.41 0.45
Std.Errors Robust Robust Robust
+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001 
# and we compare within waves
modelsummary(list(glm1_02,glm2_02),
             vcov ="robust",
             group = term ~ model,
             statistic = "std.error",
             stars = TRUE)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Model 1 Model 2
(Intercept) 13.502*** −0.209
(0.594) (0.291)
tech 0.323+ 0.056
(0.188) (0.106)
effdmy −4.237*** 0.030
(0.184) (0.092)
newtech −5.220*** −0.003
(0.166) (0.052)
exptdmy −0.207*
(0.093)
isocntryBE 1.338*** −0.828***
(0.256) (0.148)
isocntryDE-E 0.770** −0.677***
(0.272) (0.150)
isocntryDE-W 0.689** −1.211***
(0.264) (0.167)
isocntryDK −1.021*** 0.260+
(0.236) (0.138)
isocntryES 0.921*** −1.244***
(0.277) (0.185)
isocntryFI −0.166 0.478***
(0.258) (0.141)
isocntryFR 0.967*** −0.537***
(0.267) (0.145)
isocntryGB-GBN 0.946*** −0.717***
(0.253) (0.151)
isocntryGB-NIR 0.342 −0.680***
(0.328) (0.200)
isocntryGR −0.342 −1.460***
(0.274) (0.157)
isocntryIE 0.534+ −0.617***
(0.274) (0.160)
isocntryIT 1.257*** −0.984***
(0.256) (0.148)
isocntryLU 0.396 −0.576***
(0.312) (0.170)
isocntryNL 2.498*** −0.492**
(0.247) (0.157)
isocntryNO 0.593* −0.005
(0.261) (0.148)
isocntryPT 1.162*** −1.015***
(0.257) (0.175)
isocntrySE 1.090*** 0.010
(0.255) (0.144)
sex 0.263** 0.073
(0.089) (0.053)
age 0.003 0.005**
(0.003) (0.002)
educ −0.002 0.002
(0.002) (0.001)
evotedmy −0.278***
(0.080)
Num.Obs. 7045 7045
AIC 3854.6 9575.2
BIC 4026.1 9746.7
RMSE 0.28 0.48
Std.Errors Robust Robust
+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001 
modelsummary(list(glm1_16,glm2_16),
             vcov ="robust",
             group = term ~ model,
             statistic = "std.error",
             stars = TRUE)
Model 1 Model 2
(Intercept) 2.017*** −3.334***
(0.120) (0.132)
tech 0.118*** 0.020
(0.022) (0.022)
exptdmy 1.907***
(0.055)
isocntryBE 0.754*** −0.156
(0.105) (0.107)
isocntryBG 1.403*** −0.858***
(0.114) (0.116)
isocntryCY 0.899*** −0.304*
(0.138) (0.133)
isocntryCZ 1.134*** 0.020
(0.112) (0.105)
isocntryDE 0.128 −0.108
(0.101) (0.111)
isocntryDK 1.558*** −0.533***
(0.113) (0.111)
isocntryEE 1.511*** −0.759***
(0.113) (0.115)
isocntryES 0.704*** −0.470***
(0.106) (0.112)
isocntryFI 1.709*** 0.005
(0.122) (0.108)
isocntryFR 0.060 −0.085
(0.100) (0.108)
isocntryGB 0.988*** −0.241*
(0.107) (0.108)
isocntryGR 0.653*** −0.882***
(0.106) (0.120)
isocntryHR 0.408*** −0.106
(0.104) (0.109)
isocntryHU 0.110 −0.187+
(0.102) (0.113)
isocntryIE 1.007*** −0.508***
(0.107) (0.111)
isocntryIT 0.184+ −0.126
(0.102) (0.111)
isocntryLT 1.491*** −0.475***
(0.114) (0.110)
isocntryLU 0.169 −0.033
(0.125) (0.132)
isocntryLV 1.207*** −0.125
(0.112) (0.107)
isocntryMT 0.944*** −1.317***
(0.133) (0.170)
isocntryNL 1.083*** −0.261*
(0.110) (0.108)
isocntryPL 0.875*** −0.148
(0.107) (0.107)
isocntryPT 1.235*** −0.606***
(0.117) (0.113)
isocntryRO 0.794*** −1.202***
(0.107) (0.130)
isocntrySE 1.585*** −0.792***
(0.113) (0.114)
isocntrySI 0.296** −0.412***
(0.100) (0.114)
isocntrySK 1.191*** −0.093
(0.116) (0.108)
sex −0.086** 0.184***
(0.032) (0.032)
age −0.035*** 0.010***
(0.001) (0.001)
educ −0.003** −0.001
(0.001) (0.001)
evotedmy 1.928***
(0.055)
Num.Obs. 25376 25376
AIC 24944.9 25509.3
BIC 25213.6 25777.9
RMSE 0.40 0.41
Std.Errors Robust Robust
+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001 
modelsummary(list(glm1_18,glm2_18),
             vcov ="robust",
             group = term ~ model,
             statistic = "std.error",
             stars = TRUE)
Model 1 Model 2
(Intercept) −0.210* −0.823***
(0.104) (0.105)
tech −0.016 −0.006
(0.017) (0.017)
exptdmy 0.439*** 0.618***
(0.032) (0.033)
netuse 0.029** 0.058***
(0.010) (0.010)
isocntryBE −0.328** −0.349***
(0.101) (0.102)
isocntryBG −0.535*** −0.382***
(0.109) (0.108)
isocntryCY −0.604*** −0.789***
(0.134) (0.141)
isocntryCZ 0.194+ −0.268**
(0.099) (0.103)
isocntryDE-E 0.961*** 0.238+
(0.128) (0.127)
isocntryDE-W 0.630*** 0.042
(0.101) (0.104)
isocntryDK −0.382*** −0.443***
(0.102) (0.103)
isocntryEE 0.228* 0.306**
(0.102) (0.101)
isocntryES −1.124*** −0.938***
(0.118) (0.113)
isocntryFI −0.601*** −0.059
(0.104) (0.100)
isocntryFR −0.666*** −0.420***
(0.107) (0.103)
isocntryGB −0.547*** −0.879***
(0.105) (0.111)
isocntryGR −0.587*** −0.366***
(0.107) (0.106)
isocntryHR −0.151 0.079
(0.099) (0.098)
isocntryHU −0.112 −0.394***
(0.101) (0.104)
isocntryIE −0.908*** −0.954***
(0.110) (0.111)
isocntryIT −0.557*** −0.406***
(0.107) (0.106)
isocntryLT −0.293** −0.440***
(0.105) (0.107)
isocntryLU −0.317* −0.246+
(0.132) (0.130)
isocntryLV −0.463*** −0.726***
(0.104) (0.107)
isocntryMT −0.528*** −0.583***
(0.133) (0.135)
isocntryNL −0.179+ −0.243*
(0.100) (0.100)
isocntryPL 0.070 −0.310**
(0.100) (0.104)
isocntryPT −0.248* −0.247*
(0.106) (0.106)
isocntryRO −0.272** −0.453***
(0.103) (0.106)
isocntrySE 0.178+ 0.188+
(0.097) (0.097)
isocntrySI 0.378*** 0.257*
(0.101) (0.102)
isocntrySK 0.409*** 0.262*
(0.104) (0.105)
sex −0.238*** −0.078**
(0.029) (0.029)
age −0.004** 0.001
(0.001) (0.001)
educ 0.000 −0.001
(0.001) (0.001)
Num.Obs. 23336 23208
AIC 28294.6 27952.6
BIC 28576.6 28234.4
RMSE 0.46 0.45
Std.Errors Robust Robust
+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001

Testing Performance and GOFs

We test this with performance package.

library("performance")
library("blorr")

results02 <- compare_performance(glm1_02,glm2_02, rank = T)
results02
## # Comparison of Model Performance Indices
## 
## Name    | Model | Tjur's R2 |  RMSE | Sigma | Log_loss | Score_log |   PCP | AIC weights | BIC weights | Performance-Score
## --------------------------------------------------------------------------------------------------------------------------
## glm1_02 |   glm |     0.643 | 0.279 | 0.721 |    0.256 |      -Inf | 0.841 |        1.00 |        1.00 |            85.71%
## glm2_02 |   glm |     0.079 | 0.478 | 1.140 |    0.648 |      -Inf | 0.544 |     < 0.001 |     < 0.001 |            14.29%
plot(results02)

# model glm1_02 with evotedmy as DV is the best fit for 2002.

results16 <- compare_performance(glm1_16,glm2_16, rank = T)
results16
## # Comparison of Model Performance Indices
## 
## Name    | Model | Tjur's R2 |  RMSE | Sigma | Log_loss | Score_log |   PCP | AIC weights | BIC weights | Performance-Score
## --------------------------------------------------------------------------------------------------------------------------
## glm1_16 |   glm |     0.152 | 0.401 | 0.991 |    0.490 |      -Inf | 0.677 |        1.00 |        1.00 |            85.71%
## glm2_16 |   glm |     0.081 | 0.407 | 1.002 |    0.501 |      -Inf | 0.669 |     < 0.001 |     < 0.001 |            14.29%
plot(results16)

results18 <- compare_performance(glm1_18,glm2_18,glm3_18, rank = T)
results18
## # Comparison of Model Performance Indices
## 
## Name    | Model | Tjur's R2 |  RMSE | Sigma | Log_loss | Score_log |   PCP | AIC weights | BIC weights | Performance-Score
## --------------------------------------------------------------------------------------------------------------------------
## glm3_18 |   glm |     0.213 | 0.443 | 1.073 |    0.574 |      -Inf | 0.607 |        1.00 |        1.00 |            85.71%
## glm2_18 |   glm |     0.041 | 0.454 | 1.097 |    0.601 |      -Inf | 0.588 |     < 0.001 |     < 0.001 |            18.92%
## glm1_18 |   glm |     0.051 | 0.456 | 1.101 |    0.605 |      -Inf | 0.584 |     < 0.001 |     < 0.001 |            15.07%
plot(results18)

# we also test for the fit stats from blorr package
blr_model_fit_stats(glm02_NULL)
##                                Model Fit Statistics                                
## ----------------------------------------------------------------------------------
## Log-Lik Intercept Only:     -9252.163    Log-Lik Full Model:             -9655.428 
## Deviance(13349):            18483.152    LR(0):                           -806.530 
##                                          Prob > LR:                          1.000 
## MCFadden's R2                  -0.044    McFadden's Adj R2:                 -0.044 
## ML (Cox-Snell) R2:             -0.049    Cragg-Uhler(Nagelkerke) R2:        -0.074 
## McKelvey & Zavoina's R2:        0.000    Efron's R2:                         0.000 
## Count R2:                       0.507    Adj Count R2:                       0.000 
## BIC:                        19320.354    AIC:                            19312.855 
## ----------------------------------------------------------------------------------
blr_model_fit_stats(glm1_02) # best model
##                               Model Fit Statistics                                
## ---------------------------------------------------------------------------------
## Log-Lik Intercept Only:     -9252.163    Log-Lik Full Model:            -1902.306 
## Deviance(7020):              3646.006    LR(24):                        14699.714 
##                                          Prob > LR:                         0.000 
## MCFadden's R2                   0.794    McFadden's Adj R2:                 0.792 
## ML (Cox-Snell) R2:              0.583    Cragg-Uhler(Nagelkerke) R2:        0.874 
## McKelvey & Zavoina's R2:        0.839    Efron's R2:                        0.648 
## Count R2:                       0.894    Adj Count R2:                      0.678 
## BIC:                         4026.114    AIC:                            3854.612 
## ---------------------------------------------------------------------------------
blr_model_fit_stats(glm2_02)
##                               Model Fit Statistics                                
## ---------------------------------------------------------------------------------
## Log-Lik Intercept Only:     -7566.900    Log-Lik Full Model:            -4762.612 
## Deviance(7020):              9127.784    LR(24):                         5608.575 
##                                          Prob > LR:                         0.000 
## MCFadden's R2                   0.371    McFadden's Adj R2:                 0.367 
## ML (Cox-Snell) R2:              0.284    Cragg-Uhler(Nagelkerke) R2:        0.478 
## McKelvey & Zavoina's R2:        0.099    Efron's R2:                        0.078 
## Count R2:                       0.628    Adj Count R2:                      0.175 
## BIC:                         9746.727    AIC:                            9575.225 
## ---------------------------------------------------------------------------------
# we also test for the fit stats from blorr package
blr_model_fit_stats(glm16_NULL)
##                                Model Fit Statistics                                 
## -----------------------------------------------------------------------------------
## Log-Lik Intercept Only:     -14802.163    Log-Lik Full Model:            -14802.163 
## Deviance(25867):             29604.325    LR(0):                              0.000 
##                                           Prob > LR:                          1.000 
## MCFadden's R2                    0.000    McFadden's Adj R2:                  0.000 
## ML (Cox-Snell) R2:               0.000    Cragg-Uhler(Nagelkerke) R2:         0.000 
## McKelvey & Zavoina's R2:         0.000    Efron's R2:                         0.000 
## Count R2:                        0.741    Adj Count R2:                       0.000 
## BIC:                         29614.486    AIC:                            29606.325 
## -----------------------------------------------------------------------------------
blr_model_fit_stats(glm1_16) # best model
##                                Model Fit Statistics                                 
## -----------------------------------------------------------------------------------
## Log-Lik Intercept Only:     -14802.163    Log-Lik Full Model:            -12439.446 
## Deviance(25343):             24878.892    LR(32):                          4725.434 
##                                           Prob > LR:                          0.000 
## MCFadden's R2                    0.160    McFadden's Adj R2:                  0.157 
## ML (Cox-Snell) R2:               0.163    Cragg-Uhler(Nagelkerke) R2:         0.243 
## McKelvey & Zavoina's R2:         0.268    Efron's R2:                         0.154 
## Count R2:                        0.761    Adj Count R2:                       0.067 
## BIC:                         25213.563    AIC:                            24944.892 
## -----------------------------------------------------------------------------------
blr_model_fit_stats(glm2_16)
##                                Model Fit Statistics                                 
## -----------------------------------------------------------------------------------
## Log-Lik Intercept Only:     -14038.528    Log-Lik Full Model:            -12721.635 
## Deviance(25343):             25443.270    LR(32):                          2633.786 
##                                           Prob > LR:                          0.000 
## MCFadden's R2                    0.094    McFadden's Adj R2:                  0.091 
## ML (Cox-Snell) R2:               0.094    Cragg-Uhler(Nagelkerke) R2:         0.145 
## McKelvey & Zavoina's R2:         0.190    Efron's R2:                         0.083 
## Count R2:                        0.764    Adj Count R2:                       0.000 
## BIC:                         25777.941    AIC:                            25509.270 
## -----------------------------------------------------------------------------------
# we also test for the fit stats from blorr package
blr_model_fit_stats(glm18_NULL1)
##                                Model Fit Statistics                                 
## -----------------------------------------------------------------------------------
## Log-Lik Intercept Only:     -16708.666    Log-Lik Full Model:            -16708.666 
## Deviance(26246):             33417.332    LR(0):                              0.000 
##                                           Prob > LR:                          1.000 
## MCFadden's R2                    0.000    McFadden's Adj R2:                  0.000 
## ML (Cox-Snell) R2:               0.000    Cragg-Uhler(Nagelkerke) R2:         0.000 
## McKelvey & Zavoina's R2:         0.000    Efron's R2:                         0.000 
## Count R2:                        0.667    Adj Count R2:                       0.000 
## BIC:                         33427.507    AIC:                            33419.332 
## -----------------------------------------------------------------------------------
blr_model_fit_stats(glm18_NULL2) # maybe better DV?
##                                Model Fit Statistics                                 
## -----------------------------------------------------------------------------------
## Log-Lik Intercept Only:     -16374.321    Log-Lik Full Model:            -16374.321 
## Deviance(26004):             32748.642    LR(0):                              0.000 
##                                           Prob > LR:                          1.000 
## MCFadden's R2                    0.000    McFadden's Adj R2:                  0.000 
## ML (Cox-Snell) R2:               0.000    Cragg-Uhler(Nagelkerke) R2:         0.000 
## McKelvey & Zavoina's R2:         0.000    Efron's R2:                         0.000 
## Count R2:                        0.676    Adj Count R2:                       0.000 
## BIC:                         32758.808    AIC:                            32750.642 
## -----------------------------------------------------------------------------------
blr_model_fit_stats(glm1_18) 
##                                Model Fit Statistics                                 
## -----------------------------------------------------------------------------------
## Log-Lik Intercept Only:     -16708.666    Log-Lik Full Model:            -14112.276 
## Deviance(23301):             28224.553    LR(34):                          5192.780 
##                                           Prob > LR:                          0.000 
## MCFadden's R2                    0.155    McFadden's Adj R2:                  0.153 
## ML (Cox-Snell) R2:               0.172    Cragg-Uhler(Nagelkerke) R2:         0.245 
## McKelvey & Zavoina's R2:         0.070    Efron's R2:                         0.051 
## Count R2:                        0.682    Adj Count R2:                       0.020 
## BIC:                         28576.574    AIC:                            28294.553 
## -----------------------------------------------------------------------------------
blr_model_fit_stats(glm2_18) 
##                                Model Fit Statistics                                 
## -----------------------------------------------------------------------------------
## Log-Lik Intercept Only:     -16374.321    Log-Lik Full Model:            -13941.281 
## Deviance(23173):             27882.563    LR(34):                          4866.079 
##                                           Prob > LR:                          0.000 
## MCFadden's R2                    0.149    McFadden's Adj R2:                  0.146 
## ML (Cox-Snell) R2:               0.162    Cragg-Uhler(Nagelkerke) R2:         0.233 
## McKelvey & Zavoina's R2:         0.059    Efron's R2:                         0.042 
## Count R2:                        0.688    Adj Count R2:                       0.003 
## BIC:                         28234.392    AIC:                            27952.563 
## -----------------------------------------------------------------------------------
blr_model_fit_stats(glm3_18) # best model
##                                Model Fit Statistics                                 
## -----------------------------------------------------------------------------------
## Log-Lik Intercept Only:     -16629.252    Log-Lik Full Model:            -13403.644 
## Deviance(23301):             26807.287    LR(34):                          6451.217 
##                                           Prob > LR:                          0.000 
## MCFadden's R2                    0.194    McFadden's Adj R2:                  0.192 
## ML (Cox-Snell) R2:               0.209    Cragg-Uhler(Nagelkerke) R2:         0.298 
## McKelvey & Zavoina's R2:         0.308    Efron's R2:                         0.214 
## Count R2:                        0.692    Adj Count R2:                       0.379 
## BIC:                         27159.309    AIC:                            26877.287 
## -----------------------------------------------------------------------------------

The test says us that working with the Logreg model, so the dummy variable on evoting is a better fit, than the model on technical awareness and ability. Thus, we further work with this DV and add techmean variable as IV in 2002, and keep the tech variable as IV also.

Plotting with Sjstat and Sjplot

detach("package:performance")
library(sjstats)
## 
## Attaching package: 'sjstats'
## The following object is masked from 'package:psych':
## 
##     phi
## The following object is masked from 'package:gmodels':
## 
##     ci
## The following object is masked from 'package:survey':
## 
##     cv
library(sjPlot)
tab_model(lm2_02,lm1_16,lm1_18, auto.label = F, show.se = T)
  exptdmy exptdmy exptdmy
Predictors Estimates std. Error CI p Estimates std. Error CI p Estimates std. Error CI p
(Intercept) 0.29 0.07 0.16 – 0.42 <0.001 -0.04 0.02 -0.08 – -0.00 0.041 0.76 0.02 0.72 – 0.80 <0.001
tech 0.01 0.02 -0.03 – 0.06 0.610 0.00 0.00 -0.00 – 0.01 0.280 0.00 0.00 -0.00 – 0.01 0.216
effdmy 0.00 0.02 -0.04 – 0.04 0.901
newtech -0.00 0.01 -0.03 – 0.02 0.777
evotedmy -0.07 0.02 -0.10 – -0.03 <0.001 0.25 0.01 0.24 – 0.26 <0.001 0.09 0.01 0.07 – 0.10 <0.001
countryDenmark 0.25 0.03 0.19 – 0.32 <0.001
countryGermany -0.03 0.03 -0.09 – 0.03 0.318
countryGreece -0.14 0.03 -0.20 – -0.07 <0.001
countrySpain -0.10 0.04 -0.17 – -0.02 0.015
countryFrance 0.06 0.03 -0.01 – 0.12 0.091
countryIreland 0.03 0.03 -0.04 – 0.10 0.375
countryItaly -0.04 0.03 -0.11 – 0.02 0.202
countryLuxembourg 0.06 0.04 -0.01 – 0.14 0.111
countryNetherlands 0.07 0.04 0.00 – 0.14 0.042
countryPortugal -0.05 0.04 -0.12 – 0.03 0.211
countryUnited Kindom 0.03 0.03 -0.03 – 0.10 0.317
countryAustria 0.20 0.03 0.13 – 0.26 <0.001
countrySweden 0.20 0.03 0.13 – 0.26 <0.001
countryFinland 0.30 0.03 0.24 – 0.37 <0.001
sex 0.02 0.01 -0.01 – 0.04 0.138 0.03 0.01 0.02 – 0.04 <0.001 -0.01 0.01 -0.02 – 0.00 0.242
age 0.00 0.00 0.00 – 0.00 0.024 0.00 0.00 0.00 – 0.00 <0.001 -0.00 0.00 -0.00 – -0.00 <0.001
educ 0.00 0.00 -0.00 – 0.00 0.100 -0.00 0.00 -0.00 – 0.00 0.239 -0.00 0.00 -0.00 – 0.00 0.233
isocntryBE -0.02 0.02 -0.06 – 0.01 0.226 0.19 0.02 0.14 – 0.23 <0.001
isocntryBG -0.14 0.02 -0.17 – -0.10 <0.001 0.04 0.02 -0.00 – 0.08 0.076
isocntryCY -0.05 0.02 -0.09 – -0.00 0.035 0.12 0.03 0.06 – 0.17 <0.001
isocntryCZ 0.01 0.02 -0.02 – 0.05 0.467 0.11 0.02 0.06 – 0.15 <0.001
isocntryDE -0.02 0.02 -0.05 – 0.02 0.356
isocntryDK -0.09 0.02 -0.13 – -0.05 <0.001 0.20 0.02 0.16 – 0.24 <0.001
isocntryEE -0.13 0.02 -0.16 – -0.09 <0.001 0.29 0.02 0.25 – 0.33 <0.001
isocntryES -0.08 0.02 -0.11 – -0.04 <0.001 0.06 0.02 0.02 – 0.10 0.006
isocntryFI 0.01 0.02 -0.02 – 0.05 0.481 0.22 0.02 0.18 – 0.26 <0.001
isocntryFR -0.01 0.02 -0.05 – 0.02 0.480 0.10 0.02 0.05 – 0.14 <0.001
isocntryGB -0.04 0.02 -0.07 – -0.00 0.044 0.18 0.02 0.14 – 0.23 <0.001
isocntryGR -0.13 0.02 -0.17 – -0.09 <0.001 -0.02 0.02 -0.06 – 0.02 0.321
isocntryHR -0.01 0.02 -0.05 – 0.02 0.440 0.16 0.02 0.12 – 0.20 <0.001
isocntryHU -0.03 0.02 -0.07 – 0.01 0.106 -0.06 0.02 -0.10 – -0.02 0.003
isocntryIE -0.08 0.02 -0.12 – -0.05 <0.001 0.14 0.02 0.09 – 0.18 <0.001
isocntryIT -0.02 0.02 -0.06 – 0.02 0.291 -0.03 0.02 -0.07 – 0.01 0.122
isocntryLT -0.08 0.02 -0.12 – -0.04 <0.001 0.21 0.02 0.17 – 0.25 <0.001
isocntryLU -0.00 0.02 -0.05 – 0.04 0.884 0.04 0.03 -0.01 – 0.09 0.152
isocntryLV -0.01 0.02 -0.05 – 0.02 0.450 0.23 0.02 0.19 – 0.27 <0.001
isocntryMT -0.19 0.02 -0.23 – -0.14 <0.001 0.17 0.03 0.12 – 0.22 <0.001
isocntryNL -0.04 0.02 -0.08 – -0.00 0.035 0.33 0.02 0.29 – 0.37 <0.001
isocntryPL -0.02 0.02 -0.06 – 0.02 0.298 0.13 0.02 0.09 – 0.17 <0.001
isocntryPT -0.10 0.02 -0.14 – -0.06 <0.001 0.01 0.02 -0.04 – 0.05 0.724
isocntryRO -0.17 0.02 -0.20 – -0.13 <0.001 0.01 0.02 -0.03 – 0.05 0.677
isocntrySE -0.13 0.02 -0.17 – -0.09 <0.001 0.06 0.02 0.01 – 0.10 0.008
isocntrySI -0.07 0.02 -0.10 – -0.03 <0.001 0.06 0.02 0.02 – 0.11 0.003
isocntrySK -0.01 0.02 -0.04 – 0.03 0.675 0.14 0.02 0.10 – 0.18 <0.001
netuse -0.07 0.00 -0.08 – -0.07 <0.001
isocntryDE-E -0.00 0.03 -0.06 – 0.05 0.907
isocntryDE-W -0.07 0.02 -0.11 – -0.03 0.002
Observations 6619 25376 23336
R2 / R2 adjusted 0.080 / 0.077 0.076 / 0.075 0.209 / 0.207 
GLM1 <- list(glm1_02,glm1_16,glm1_18) # evotedmy as DV all years
GLM2 <- list(glm2_02,glm2_16,glm2_18) # exptdmy as DV all years
lm <- list(lm2_02,lm1_16,lm1_18) # exptdmy as DV

GLM02 <- list(glm1_02,glm2_02) # evotedmy as DV 2002
GLM16 <- list(glm1_16,glm2_16) # exptdmy as DV 2016
GLM18 <- list(glm1_18,glm2_18,glm3_18) # exptdmy as DV 2018

lm <- list(lm2_02,lm1_16,lm1_18) # exptdmy as DV

tab_model(GLM1, auto.label = F, show.se = T)
## Profiled confidence intervals may take longer time to compute. Use
##   'ci_method="wald"' for faster computation of CIs.
## Profiled confidence intervals may take longer time to compute. Use
##   'ci_method="wald"' for faster computation of CIs.
  DV DV DV1
Predictors Odds Ratios std. Error CI p Odds Ratios std. Error CI p Odds Ratios std. Error CI p
(Intercept) 730539.23 389275.57 261161.55 – 2110091.12 <0.001 7.52 0.89 5.97 – 9.48 <0.001 0.81 0.08 0.66 – 0.99 0.045
tech 1.38 0.25 0.97 – 1.96 0.071 1.13 0.02 1.08 – 1.18 <0.001 0.98 0.02 0.95 – 1.02 0.332
effdmy 0.01 0.00 0.01 – 0.02 <0.001
newtech 0.01 0.00 0.00 – 0.01 <0.001
exptdmy 0.81 0.07 0.68 – 0.97 0.018 6.73 0.36 6.07 – 7.49 <0.001 1.55 0.05 1.46 – 1.65 <0.001
isocntryBE 3.81 0.97 2.32 – 6.28 <0.001 2.13 0.23 1.73 – 2.62 <0.001 0.72 0.07 0.59 – 0.88 0.001
isocntryDE-E 2.16 0.56 1.31 – 3.58 0.003 2.62 0.33 2.04 – 3.36 <0.001
isocntryDE-W 1.99 0.48 1.24 – 3.21 0.005 1.88 0.19 1.54 – 2.29 <0.001
isocntryDK 0.36 0.08 0.23 – 0.57 <0.001 4.75 0.55 3.80 – 5.97 <0.001 0.68 0.07 0.56 – 0.83 <0.001
isocntryES 2.51 0.67 1.49 – 4.24 0.001 2.02 0.21 1.64 – 2.49 <0.001 0.32 0.04 0.26 – 0.41 <0.001
isocntryFI 0.85 0.19 0.54 – 1.32 0.466 5.52 0.68 4.35 – 7.05 <0.001 0.55 0.06 0.45 – 0.67 <0.001
isocntryFR 2.63 0.67 1.60 – 4.32 <0.001 1.06 0.11 0.87 – 1.29 0.550 0.51 0.05 0.42 – 0.63 <0.001
isocntryGB-GBN 2.57 0.62 1.61 – 4.13 <0.001
isocntryGB-NIR 1.41 0.45 0.74 – 2.63 0.289
isocntryGR 0.71 0.18 0.43 – 1.17 0.183 1.92 0.20 1.57 – 2.36 <0.001 0.56 0.06 0.45 – 0.69 <0.001
isocntryIE 1.70 0.42 1.06 – 2.76 0.029 2.74 0.29 2.22 – 3.37 <0.001 0.40 0.04 0.32 – 0.50 <0.001
isocntryIT 3.52 0.85 2.19 – 5.67 <0.001 1.20 0.12 0.98 – 1.47 0.071 0.57 0.06 0.46 – 0.71 <0.001
isocntryLU 1.49 0.39 0.89 – 2.47 0.129 1.18 0.15 0.93 – 1.52 0.178 0.73 0.09 0.56 – 0.94 0.015
isocntryNL 12.15 3.03 7.49 – 19.91 <0.001 2.95 0.33 2.38 – 3.67 <0.001 0.84 0.08 0.69 – 1.02 0.073
isocntryNO 1.81 0.43 1.14 – 2.88 0.012
isocntryPT 3.20 0.82 1.93 – 5.30 <0.001 3.44 0.40 2.75 – 4.32 <0.001 0.78 0.08 0.63 – 0.96 0.018
isocntrySE 2.97 0.70 1.88 – 4.73 <0.001 4.88 0.56 3.90 – 6.12 <0.001 1.20 0.12 0.99 – 1.45 0.067
sex 1.30 0.11 1.10 – 1.53 0.002 0.92 0.03 0.86 – 0.98 0.007 0.79 0.02 0.74 – 0.83 <0.001
age 1.00 0.00 1.00 – 1.01 0.309 0.97 0.00 0.96 – 0.97 <0.001 1.00 0.00 0.99 – 1.00 0.001
educ 1.00 0.00 0.99 – 1.00 0.293 1.00 0.00 0.99 – 1.00 0.001 1.00 0.00 1.00 – 1.00 0.633
isocntryBG 4.07 0.45 3.27 – 5.06 <0.001 0.59 0.06 0.47 – 0.72 <0.001
isocntryCY 2.46 0.34 1.89 – 3.22 <0.001 0.55 0.07 0.42 – 0.71 <0.001
isocntryCZ 3.11 0.35 2.49 – 3.88 <0.001 1.21 0.12 1.00 – 1.48 0.051
isocntryDE 1.14 0.12 0.93 – 1.39 0.212
isocntryEE 4.53 0.53 3.62 – 5.70 <0.001 1.26 0.13 1.03 – 1.53 0.026
isocntryGB 2.69 0.29 2.17 – 3.34 <0.001 0.58 0.06 0.47 – 0.71 <0.001
isocntryHR 1.50 0.16 1.23 – 1.85 <0.001 0.86 0.09 0.71 – 1.05 0.131
isocntryHU 1.12 0.11 0.92 – 1.36 0.274 0.89 0.09 0.73 – 1.09 0.265
isocntryLT 4.44 0.52 3.54 – 5.59 <0.001 0.75 0.08 0.61 – 0.92 0.005
isocntryLV 3.34 0.38 2.68 – 4.18 <0.001 0.63 0.07 0.51 – 0.77 <0.001
isocntryMT 2.57 0.34 1.98 – 3.34 <0.001 0.59 0.08 0.45 – 0.76 <0.001
isocntryPL 2.40 0.26 1.94 – 2.96 <0.001 1.07 0.11 0.88 – 1.31 0.485
isocntryRO 2.21 0.23 1.80 – 2.72 <0.001 0.76 0.08 0.62 – 0.93 0.008
isocntrySI 1.35 0.14 1.10 – 1.64 0.003 1.46 0.15 1.20 – 1.78 <0.001
isocntrySK 3.29 0.38 2.63 – 4.13 <0.001 1.51 0.16 1.23 – 1.84 <0.001
netuse 1.03 0.01 1.01 – 1.05 0.002
Observations 7045 25376 23336
R2 Tjur 0.643 0.152 0.051 
tab_model(GLM2, auto.label = F, show.se = T)
## Profiled confidence intervals may take longer time to compute. Use
##   'ci_method="wald"' for faster computation of CIs.
## Profiled confidence intervals may take longer time to compute. Use
##   'ci_method="wald"' for faster computation of CIs.
  exptdmy exptdmy DV2
Predictors Odds Ratios std. Error CI p Odds Ratios std. Error CI p Odds Ratios std. Error CI p
(Intercept) 0.81 0.22 0.47 – 1.39 0.444 0.04 0.00 0.03 – 0.05 <0.001 0.44 0.05 0.36 – 0.54 <0.001
tech 1.06 0.11 0.87 – 1.29 0.580 1.02 0.02 0.98 – 1.06 0.341 0.99 0.02 0.96 – 1.03 0.745
effdmy 1.03 0.09 0.87 – 1.22 0.726
newtech 1.00 0.05 0.90 – 1.10 0.960
evotedmy 0.76 0.06 0.65 – 0.88 <0.001 6.87 0.37 6.19 – 7.65 <0.001
isocntryBE 0.44 0.06 0.33 – 0.57 <0.001 0.86 0.09 0.69 – 1.06 0.152 0.71 0.07 0.58 – 0.86 0.001
isocntryDE-E 0.51 0.07 0.38 – 0.67 <0.001 1.27 0.16 0.99 – 1.63 0.063
isocntryDE-W 0.30 0.05 0.22 – 0.40 <0.001 1.04 0.11 0.85 – 1.28 0.686
isocntryDK 1.30 0.17 1.01 – 1.67 0.041 0.59 0.07 0.47 – 0.73 <0.001 0.64 0.07 0.52 – 0.79 <0.001
isocntryES 0.29 0.05 0.20 – 0.41 <0.001 0.63 0.07 0.50 – 0.78 <0.001 0.39 0.04 0.31 – 0.49 <0.001
isocntryFI 1.61 0.21 1.25 – 2.08 <0.001 1.01 0.11 0.81 – 1.25 0.962 0.94 0.09 0.77 – 1.15 0.553
isocntryFR 0.58 0.08 0.45 – 0.76 <0.001 0.92 0.10 0.74 – 1.14 0.444 0.66 0.07 0.54 – 0.80 <0.001
isocntryGB-GBN 0.49 0.07 0.37 – 0.64 <0.001
isocntryGB-NIR 0.51 0.10 0.35 – 0.73 <0.001
isocntryGR 0.23 0.03 0.17 – 0.31 <0.001 0.41 0.05 0.33 – 0.52 <0.001 0.69 0.07 0.56 – 0.85 0.001
isocntryIE 0.54 0.08 0.41 – 0.72 <0.001 0.60 0.07 0.48 – 0.75 <0.001 0.39 0.04 0.31 – 0.48 <0.001
isocntryIT 0.37 0.05 0.28 – 0.49 <0.001 0.88 0.10 0.71 – 1.10 0.256 0.67 0.07 0.54 – 0.82 <0.001
isocntryLU 0.56 0.08 0.42 – 0.75 <0.001 0.97 0.13 0.74 – 1.26 0.807 0.78 0.10 0.61 – 1.01 0.056
isocntryNL 0.61 0.09 0.46 – 0.82 0.001 0.77 0.08 0.62 – 0.95 0.017 0.78 0.08 0.64 – 0.95 0.015
isocntryNO 1.00 0.14 0.76 – 1.30 0.971
isocntryPT 0.36 0.06 0.26 – 0.50 <0.001 0.55 0.06 0.44 – 0.68 <0.001 0.78 0.08 0.63 – 0.96 0.019
isocntrySE 1.01 0.13 0.78 – 1.31 0.942 0.45 0.05 0.36 – 0.57 <0.001 1.21 0.12 1.00 – 1.46 0.055
sex 1.08 0.05 0.97 – 1.19 0.147 1.20 0.04 1.13 – 1.28 <0.001 0.93 0.03 0.87 – 0.98 0.007
age 1.01 0.00 1.00 – 1.01 0.003 1.01 0.00 1.01 – 1.01 <0.001 1.00 0.00 1.00 – 1.00 0.617
educ 1.00 0.00 1.00 – 1.00 0.163 1.00 0.00 1.00 – 1.00 0.205 1.00 0.00 1.00 – 1.00 0.161
isocntryBG 0.42 0.05 0.34 – 0.53 <0.001 0.68 0.07 0.55 – 0.84 <0.001
isocntryCY 0.74 0.10 0.57 – 0.96 0.024 0.45 0.06 0.34 – 0.60 <0.001
isocntryCZ 1.02 0.11 0.83 – 1.26 0.852 0.76 0.08 0.63 – 0.94 0.009
isocntryDE 0.90 0.10 0.72 – 1.12 0.338
isocntryEE 0.47 0.05 0.37 – 0.59 <0.001 1.36 0.14 1.11 – 1.66 0.003
isocntryGB 0.79 0.09 0.63 – 0.97 0.028 0.42 0.05 0.33 – 0.51 <0.001
isocntryHR 0.90 0.10 0.72 – 1.12 0.340 1.08 0.11 0.89 – 1.31 0.423
isocntryHU 0.83 0.09 0.66 – 1.03 0.098 0.67 0.07 0.55 – 0.83 <0.001
isocntryLT 0.62 0.07 0.50 – 0.77 <0.001 0.64 0.07 0.52 – 0.79 <0.001
isocntryLV 0.88 0.10 0.71 – 1.09 0.247 0.48 0.05 0.39 – 0.60 <0.001
isocntryMT 0.27 0.05 0.19 – 0.37 <0.001 0.56 0.08 0.43 – 0.73 <0.001
isocntryPL 0.86 0.09 0.70 – 1.07 0.174 0.73 0.08 0.60 – 0.90 0.003
isocntryRO 0.30 0.04 0.23 – 0.39 <0.001 0.64 0.07 0.52 – 0.78 <0.001
isocntrySI 0.66 0.08 0.53 – 0.83 <0.001 1.29 0.13 1.06 – 1.58 0.012
isocntrySK 0.91 0.10 0.74 – 1.13 0.393 1.30 0.14 1.06 – 1.59 0.012
exptdmy 1.85 0.06 1.74 – 1.98 <0.001
netuse 1.06 0.01 1.04 – 1.08 <0.001
Observations 7045 25376 23208
R2 Tjur 0.079 0.081 0.041 
tab_model(GLM02, auto.label = F, show.se = T)
  DV exptdmy
Predictors Odds Ratios std. Error CI p Odds Ratios std. Error CI p
(Intercept) 730539.23 389275.57 261161.55 – 2110091.12 <0.001 0.81 0.22 0.47 – 1.39 0.444
tech 1.38 0.25 0.97 – 1.96 0.071 1.06 0.11 0.87 – 1.29 0.580
effdmy 0.01 0.00 0.01 – 0.02 <0.001 1.03 0.09 0.87 – 1.22 0.726
newtech 0.01 0.00 0.00 – 0.01 <0.001 1.00 0.05 0.90 – 1.10 0.960
exptdmy 0.81 0.07 0.68 – 0.97 0.018
isocntryBE 3.81 0.97 2.32 – 6.28 <0.001 0.44 0.06 0.33 – 0.57 <0.001
isocntryDE-E 2.16 0.56 1.31 – 3.58 0.003 0.51 0.07 0.38 – 0.67 <0.001
isocntryDE-W 1.99 0.48 1.24 – 3.21 0.005 0.30 0.05 0.22 – 0.40 <0.001
isocntryDK 0.36 0.08 0.23 – 0.57 <0.001 1.30 0.17 1.01 – 1.67 0.041
isocntryES 2.51 0.67 1.49 – 4.24 0.001 0.29 0.05 0.20 – 0.41 <0.001
isocntryFI 0.85 0.19 0.54 – 1.32 0.466 1.61 0.21 1.25 – 2.08 <0.001
isocntryFR 2.63 0.67 1.60 – 4.32 <0.001 0.58 0.08 0.45 – 0.76 <0.001
isocntryGB-GBN 2.57 0.62 1.61 – 4.13 <0.001 0.49 0.07 0.37 – 0.64 <0.001
isocntryGB-NIR 1.41 0.45 0.74 – 2.63 0.289 0.51 0.10 0.35 – 0.73 <0.001
isocntryGR 0.71 0.18 0.43 – 1.17 0.183 0.23 0.03 0.17 – 0.31 <0.001
isocntryIE 1.70 0.42 1.06 – 2.76 0.029 0.54 0.08 0.41 – 0.72 <0.001
isocntryIT 3.52 0.85 2.19 – 5.67 <0.001 0.37 0.05 0.28 – 0.49 <0.001
isocntryLU 1.49 0.39 0.89 – 2.47 0.129 0.56 0.08 0.42 – 0.75 <0.001
isocntryNL 12.15 3.03 7.49 – 19.91 <0.001 0.61 0.09 0.46 – 0.82 0.001
isocntryNO 1.81 0.43 1.14 – 2.88 0.012 1.00 0.14 0.76 – 1.30 0.971
isocntryPT 3.20 0.82 1.93 – 5.30 <0.001 0.36 0.06 0.26 – 0.50 <0.001
isocntrySE 2.97 0.70 1.88 – 4.73 <0.001 1.01 0.13 0.78 – 1.31 0.942
sex 1.30 0.11 1.10 – 1.53 0.002 1.08 0.05 0.97 – 1.19 0.147
age 1.00 0.00 1.00 – 1.01 0.309 1.01 0.00 1.00 – 1.01 0.003
educ 1.00 0.00 0.99 – 1.00 0.293 1.00 0.00 1.00 – 1.00 0.163
evotedmy 0.76 0.06 0.65 – 0.88 <0.001
Observations 7045 7045
R2 Tjur 0.643 0.079 
tab_model(GLM16, auto.label = F, show.se = T)
## Profiled confidence intervals may take longer time to compute. Use
##   'ci_method="wald"' for faster computation of CIs.
## Profiled confidence intervals may take longer time to compute. Use
##   'ci_method="wald"' for faster computation of CIs.
  DV exptdmy
Predictors Odds Ratios std. Error CI p Odds Ratios std. Error CI p
(Intercept) 7.52 0.89 5.97 – 9.48 <0.001 0.04 0.00 0.03 – 0.05 <0.001
tech 1.13 0.02 1.08 – 1.18 <0.001 1.02 0.02 0.98 – 1.06 0.341
exptdmy 6.73 0.36 6.07 – 7.49 <0.001
isocntryBE 2.13 0.23 1.73 – 2.62 <0.001 0.86 0.09 0.69 – 1.06 0.152
isocntryBG 4.07 0.45 3.27 – 5.06 <0.001 0.42 0.05 0.34 – 0.53 <0.001
isocntryCY 2.46 0.34 1.89 – 3.22 <0.001 0.74 0.10 0.57 – 0.96 0.024
isocntryCZ 3.11 0.35 2.49 – 3.88 <0.001 1.02 0.11 0.83 – 1.26 0.852
isocntryDE 1.14 0.12 0.93 – 1.39 0.212 0.90 0.10 0.72 – 1.12 0.338
isocntryDK 4.75 0.55 3.80 – 5.97 <0.001 0.59 0.07 0.47 – 0.73 <0.001
isocntryEE 4.53 0.53 3.62 – 5.70 <0.001 0.47 0.05 0.37 – 0.59 <0.001
isocntryES 2.02 0.21 1.64 – 2.49 <0.001 0.63 0.07 0.50 – 0.78 <0.001
isocntryFI 5.52 0.68 4.35 – 7.05 <0.001 1.01 0.11 0.81 – 1.25 0.962
isocntryFR 1.06 0.11 0.87 – 1.29 0.550 0.92 0.10 0.74 – 1.14 0.444
isocntryGB 2.69 0.29 2.17 – 3.34 <0.001 0.79 0.09 0.63 – 0.97 0.028
isocntryGR 1.92 0.20 1.57 – 2.36 <0.001 0.41 0.05 0.33 – 0.52 <0.001
isocntryHR 1.50 0.16 1.23 – 1.85 <0.001 0.90 0.10 0.72 – 1.12 0.340
isocntryHU 1.12 0.11 0.92 – 1.36 0.274 0.83 0.09 0.66 – 1.03 0.098
isocntryIE 2.74 0.29 2.22 – 3.37 <0.001 0.60 0.07 0.48 – 0.75 <0.001
isocntryIT 1.20 0.12 0.98 – 1.47 0.071 0.88 0.10 0.71 – 1.10 0.256
isocntryLT 4.44 0.52 3.54 – 5.59 <0.001 0.62 0.07 0.50 – 0.77 <0.001
isocntryLU 1.18 0.15 0.93 – 1.52 0.178 0.97 0.13 0.74 – 1.26 0.807
isocntryLV 3.34 0.38 2.68 – 4.18 <0.001 0.88 0.10 0.71 – 1.09 0.247
isocntryMT 2.57 0.34 1.98 – 3.34 <0.001 0.27 0.05 0.19 – 0.37 <0.001
isocntryNL 2.95 0.33 2.38 – 3.67 <0.001 0.77 0.08 0.62 – 0.95 0.017
isocntryPL 2.40 0.26 1.94 – 2.96 <0.001 0.86 0.09 0.70 – 1.07 0.174
isocntryPT 3.44 0.40 2.75 – 4.32 <0.001 0.55 0.06 0.44 – 0.68 <0.001
isocntryRO 2.21 0.23 1.80 – 2.72 <0.001 0.30 0.04 0.23 – 0.39 <0.001
isocntrySE 4.88 0.56 3.90 – 6.12 <0.001 0.45 0.05 0.36 – 0.57 <0.001
isocntrySI 1.35 0.14 1.10 – 1.64 0.003 0.66 0.08 0.53 – 0.83 <0.001
isocntrySK 3.29 0.38 2.63 – 4.13 <0.001 0.91 0.10 0.74 – 1.13 0.393
sex 0.92 0.03 0.86 – 0.98 0.007 1.20 0.04 1.13 – 1.28 <0.001
age 0.97 0.00 0.96 – 0.97 <0.001 1.01 0.00 1.01 – 1.01 <0.001
educ 1.00 0.00 0.99 – 1.00 0.001 1.00 0.00 1.00 – 1.00 0.205
evotedmy 6.87 0.37 6.19 – 7.65 <0.001
Observations 25376 25376
R2 Tjur 0.152 0.081 
tab_model(GLM18, auto.label = F, show.se = T)
## Profiled confidence intervals may take longer time to compute. Use
##   'ci_method="wald"' for faster computation of CIs.
## Profiled confidence intervals may take longer time to compute. Use
##   'ci_method="wald"' for faster computation of CIs.
## Profiled confidence intervals may take longer time to compute. Use
##   'ci_method="wald"' for faster computation of CIs.
  DV1 DV2 exptdmy
Predictors Odds Ratios std. Error CI p Odds Ratios std. Error CI p Odds Ratios std. Error CI p
(Intercept) 0.81 0.08 0.66 – 0.99 0.045 0.44 0.05 0.36 – 0.54 <0.001 3.57 0.38 2.90 – 4.39 <0.001
tech 0.98 0.02 0.95 – 1.02 0.332 0.99 0.02 0.96 – 1.03 0.745 1.03 0.02 0.99 – 1.06 0.130
exptdmy 1.55 0.05 1.46 – 1.65 <0.001 1.85 0.06 1.74 – 1.98 <0.001
netuse 1.03 0.01 1.01 – 1.05 0.002 1.06 0.01 1.04 – 1.08 <0.001 0.66 0.01 0.64 – 0.67 <0.001
isocntryBE 0.72 0.07 0.59 – 0.88 0.001 0.71 0.07 0.58 – 0.86 0.001 2.39 0.25 1.95 – 2.94 <0.001
isocntryBG 0.59 0.06 0.47 – 0.72 <0.001 0.68 0.07 0.55 – 0.84 <0.001 1.20 0.13 0.97 – 1.48 0.101
isocntryCY 0.55 0.07 0.42 – 0.71 <0.001 0.45 0.06 0.34 – 0.60 <0.001 1.78 0.24 1.37 – 2.31 <0.001
isocntryCZ 1.21 0.12 1.00 – 1.48 0.051 0.76 0.08 0.63 – 0.94 0.009 1.65 0.17 1.35 – 2.03 <0.001
isocntryDE-E 2.62 0.33 2.04 – 3.36 <0.001 1.27 0.16 0.99 – 1.63 0.063 0.97 0.13 0.74 – 1.26 0.804
isocntryDE-W 1.88 0.19 1.54 – 2.29 <0.001 1.04 0.11 0.85 – 1.28 0.686 0.68 0.08 0.55 – 0.85 0.001
isocntryDK 0.68 0.07 0.56 – 0.83 <0.001 0.64 0.07 0.52 – 0.79 <0.001 2.55 0.26 2.08 – 3.12 <0.001
isocntryEE 1.26 0.13 1.03 – 1.53 0.026 1.36 0.14 1.11 – 1.66 0.003 4.38 0.51 3.50 – 5.50 <0.001
isocntryES 0.32 0.04 0.26 – 0.41 <0.001 0.39 0.04 0.31 – 0.49 <0.001 1.32 0.14 1.08 – 1.62 0.008
isocntryFI 0.55 0.06 0.45 – 0.67 <0.001 0.94 0.09 0.77 – 1.15 0.553 2.88 0.30 2.35 – 3.54 <0.001
isocntryFR 0.51 0.05 0.42 – 0.63 <0.001 0.66 0.07 0.54 – 0.80 <0.001 1.59 0.16 1.30 – 1.95 <0.001
isocntryGB 0.58 0.06 0.47 – 0.71 <0.001 0.42 0.05 0.33 – 0.51 <0.001 2.42 0.26 1.96 – 2.98 <0.001
isocntryGR 0.56 0.06 0.45 – 0.69 <0.001 0.69 0.07 0.56 – 0.85 0.001 0.86 0.09 0.70 – 1.06 0.165
isocntryHR 0.86 0.09 0.71 – 1.05 0.131 1.08 0.11 0.89 – 1.31 0.423 2.14 0.22 1.74 – 2.63 <0.001
isocntryHU 0.89 0.09 0.73 – 1.09 0.265 0.67 0.07 0.55 – 0.83 <0.001 0.70 0.08 0.57 – 0.87 0.001
isocntryIE 0.40 0.04 0.32 – 0.50 <0.001 0.39 0.04 0.31 – 0.48 <0.001 1.91 0.20 1.56 – 2.33 <0.001
isocntryIT 0.57 0.06 0.46 – 0.71 <0.001 0.67 0.07 0.54 – 0.82 <0.001 0.86 0.09 0.70 – 1.05 0.140
isocntryLT 0.75 0.08 0.61 – 0.92 0.005 0.64 0.07 0.52 – 0.79 <0.001 2.95 0.33 2.37 – 3.67 <0.001
isocntryLU 0.73 0.09 0.56 – 0.94 0.015 0.78 0.10 0.61 – 1.01 0.056 1.21 0.16 0.94 – 1.56 0.132
isocntryLV 0.63 0.07 0.51 – 0.77 <0.001 0.48 0.05 0.39 – 0.60 <0.001 3.15 0.34 2.55 – 3.89 <0.001
isocntryMT 0.59 0.08 0.45 – 0.76 <0.001 0.56 0.08 0.43 – 0.73 <0.001 2.32 0.32 1.78 – 3.03 <0.001
isocntryNL 0.84 0.08 0.69 – 1.02 0.073 0.78 0.08 0.64 – 0.95 0.015 4.93 0.54 3.99 – 6.12 <0.001
isocntryPL 1.07 0.11 0.88 – 1.31 0.485 0.73 0.08 0.60 – 0.90 0.003 1.93 0.21 1.57 – 2.39 <0.001
isocntryPT 0.78 0.08 0.63 – 0.96 0.018 0.78 0.08 0.63 – 0.96 0.019 1.01 0.11 0.81 – 1.25 0.950
isocntryRO 0.76 0.08 0.62 – 0.93 0.008 0.64 0.07 0.52 – 0.78 <0.001 1.05 0.11 0.85 – 1.30 0.649
isocntrySE 1.20 0.12 0.99 – 1.45 0.067 1.21 0.12 1.00 – 1.46 0.055 1.33 0.13 1.09 – 1.62 0.005
isocntrySI 1.46 0.15 1.20 – 1.78 <0.001 1.29 0.13 1.06 – 1.58 0.012 1.35 0.15 1.09 – 1.68 0.005
isocntrySK 1.51 0.16 1.23 – 1.84 <0.001 1.30 0.14 1.06 – 1.59 0.012 2.06 0.23 1.65 – 2.57 <0.001
sex 0.79 0.02 0.74 – 0.83 <0.001 0.93 0.03 0.87 – 0.98 0.007 0.96 0.03 0.91 – 1.02 0.167
age 1.00 0.00 0.99 – 1.00 0.001 1.00 0.00 1.00 – 1.00 0.617 0.98 0.00 0.98 – 0.98 <0.001
educ 1.00 0.00 1.00 – 1.00 0.633 1.00 0.00 1.00 – 1.00 0.161 1.00 0.00 1.00 – 1.00 0.142
evotedmy 1.55 0.05 1.46 – 1.66 <0.001
Observations 23336 23208 23336
R2 Tjur 0.051 0.041 0.213 
tab_model(list(glm1_02,glm1_16,glm3_18), auto.label = F, show.se = T)
## Profiled confidence intervals may take longer time to compute. Use
##   'ci_method="wald"' for faster computation of CIs.
## Profiled confidence intervals may take longer time to compute. Use
##   'ci_method="wald"' for faster computation of CIs.
  DV DV exptdmy
Predictors Odds Ratios std. Error CI p Odds Ratios std. Error CI p Odds Ratios std. Error CI p
(Intercept) 730539.23 389275.57 261161.55 – 2110091.12 <0.001 7.52 0.89 5.97 – 9.48 <0.001 3.57 0.38 2.90 – 4.39 <0.001
tech 1.38 0.25 0.97 – 1.96 0.071 1.13 0.02 1.08 – 1.18 <0.001 1.03 0.02 0.99 – 1.06 0.130
effdmy 0.01 0.00 0.01 – 0.02 <0.001
newtech 0.01 0.00 0.00 – 0.01 <0.001
exptdmy 0.81 0.07 0.68 – 0.97 0.018 6.73 0.36 6.07 – 7.49 <0.001
isocntryBE 3.81 0.97 2.32 – 6.28 <0.001 2.13 0.23 1.73 – 2.62 <0.001 2.39 0.25 1.95 – 2.94 <0.001
isocntryDE-E 2.16 0.56 1.31 – 3.58 0.003 0.97 0.13 0.74 – 1.26 0.804
isocntryDE-W 1.99 0.48 1.24 – 3.21 0.005 0.68 0.08 0.55 – 0.85 0.001
isocntryDK 0.36 0.08 0.23 – 0.57 <0.001 4.75 0.55 3.80 – 5.97 <0.001 2.55 0.26 2.08 – 3.12 <0.001
isocntryES 2.51 0.67 1.49 – 4.24 0.001 2.02 0.21 1.64 – 2.49 <0.001 1.32 0.14 1.08 – 1.62 0.008
isocntryFI 0.85 0.19 0.54 – 1.32 0.466 5.52 0.68 4.35 – 7.05 <0.001 2.88 0.30 2.35 – 3.54 <0.001
isocntryFR 2.63 0.67 1.60 – 4.32 <0.001 1.06 0.11 0.87 – 1.29 0.550 1.59 0.16 1.30 – 1.95 <0.001
isocntryGB-GBN 2.57 0.62 1.61 – 4.13 <0.001
isocntryGB-NIR 1.41 0.45 0.74 – 2.63 0.289
isocntryGR 0.71 0.18 0.43 – 1.17 0.183 1.92 0.20 1.57 – 2.36 <0.001 0.86 0.09 0.70 – 1.06 0.165
isocntryIE 1.70 0.42 1.06 – 2.76 0.029 2.74 0.29 2.22 – 3.37 <0.001 1.91 0.20 1.56 – 2.33 <0.001
isocntryIT 3.52 0.85 2.19 – 5.67 <0.001 1.20 0.12 0.98 – 1.47 0.071 0.86 0.09 0.70 – 1.05 0.140
isocntryLU 1.49 0.39 0.89 – 2.47 0.129 1.18 0.15 0.93 – 1.52 0.178 1.21 0.16 0.94 – 1.56 0.132
isocntryNL 12.15 3.03 7.49 – 19.91 <0.001 2.95 0.33 2.38 – 3.67 <0.001 4.93 0.54 3.99 – 6.12 <0.001
isocntryNO 1.81 0.43 1.14 – 2.88 0.012
isocntryPT 3.20 0.82 1.93 – 5.30 <0.001 3.44 0.40 2.75 – 4.32 <0.001 1.01 0.11 0.81 – 1.25 0.950
isocntrySE 2.97 0.70 1.88 – 4.73 <0.001 4.88 0.56 3.90 – 6.12 <0.001 1.33 0.13 1.09 – 1.62 0.005
sex 1.30 0.11 1.10 – 1.53 0.002 0.92 0.03 0.86 – 0.98 0.007 0.96 0.03 0.91 – 1.02 0.167
age 1.00 0.00 1.00 – 1.01 0.309 0.97 0.00 0.96 – 0.97 <0.001 0.98 0.00 0.98 – 0.98 <0.001
educ 1.00 0.00 0.99 – 1.00 0.293 1.00 0.00 0.99 – 1.00 0.001 1.00 0.00 1.00 – 1.00 0.142
isocntryBG 4.07 0.45 3.27 – 5.06 <0.001 1.20 0.13 0.97 – 1.48 0.101
isocntryCY 2.46 0.34 1.89 – 3.22 <0.001 1.78 0.24 1.37 – 2.31 <0.001
isocntryCZ 3.11 0.35 2.49 – 3.88 <0.001 1.65 0.17 1.35 – 2.03 <0.001
isocntryDE 1.14 0.12 0.93 – 1.39 0.212
isocntryEE 4.53 0.53 3.62 – 5.70 <0.001 4.38 0.51 3.50 – 5.50 <0.001
isocntryGB 2.69 0.29 2.17 – 3.34 <0.001 2.42 0.26 1.96 – 2.98 <0.001
isocntryHR 1.50 0.16 1.23 – 1.85 <0.001 2.14 0.22 1.74 – 2.63 <0.001
isocntryHU 1.12 0.11 0.92 – 1.36 0.274 0.70 0.08 0.57 – 0.87 0.001
isocntryLT 4.44 0.52 3.54 – 5.59 <0.001 2.95 0.33 2.37 – 3.67 <0.001
isocntryLV 3.34 0.38 2.68 – 4.18 <0.001 3.15 0.34 2.55 – 3.89 <0.001
isocntryMT 2.57 0.34 1.98 – 3.34 <0.001 2.32 0.32 1.78 – 3.03 <0.001
isocntryPL 2.40 0.26 1.94 – 2.96 <0.001 1.93 0.21 1.57 – 2.39 <0.001
isocntryRO 2.21 0.23 1.80 – 2.72 <0.001 1.05 0.11 0.85 – 1.30 0.649
isocntrySI 1.35 0.14 1.10 – 1.64 0.003 1.35 0.15 1.09 – 1.68 0.005
isocntrySK 3.29 0.38 2.63 – 4.13 <0.001 2.06 0.23 1.65 – 2.57 <0.001
evotedmy 1.55 0.05 1.46 – 1.66 <0.001
netuse 0.66 0.01 0.64 – 0.67 <0.001
Observations 7045 25376 23336
R2 Tjur 0.643 0.152 0.213 
cols <- c("8" = "red", "4" = "blue", "6" = "darkgreen", "10" = "orange")
b <- list(geom_vline(xintercept = 0, color = 'orange'),
          annotate("rect", alpha = .1,
                   xmin = -.5, xmax = .5, 
                   ymin = -Inf, ymax = Inf),
          geom_point(aes(y = term, x = estimate), alpha = .3, 
                     size = 3, color = 'red'))

# models with evotedmy as DV
modelplot(GLM1, background = b) +
          labs(
            x = 'Coefficients',
            y = 'Terms',
            colors = cols,
            title = 'Logistic regression models ') +
          facet_grid(~model)

# models with exptdmy as DV
modelplot(GLM2, background = b) +
          labs(
            x = 'Coefficients',
            y = 'Terms',
            colors = cols,
            title = 'Logistic regression models ') +
          facet_grid(~model)

# lm models 
modelplot(lm, background = b) +
          labs(
            x = 'Coefficients',
            y = 'Terms',
            colors = cols,
            title = 'Logistic regression models ') +
          facet_grid(~model)

modelplot(GLM02, background = b) +
          labs(
            x = 'Coefficients',
            y = 'Terms',
            colors = cols,
            title = 'Logistic regression models 2002') +
          facet_grid(~model)

modelplot(GLM16, background = b) +
          labs(
            x = 'Coefficients',
            y = 'Terms',
            colors = cols,
            title = 'Logistic regression models 2016') +
          facet_grid(~model)

modelplot(GLM18, background = b) +
          labs(
            x = 'Coefficients',
            y = 'Terms',
            colors = cols,
            title = 'Logistic regression models 2018') +
          facet_grid(~model)

Stargazer Outputs

stargazer2 <- function(model, odd.ratio = F, ...) {
  if(!("list" %in% class(model))) model <- list(model)
  
  if (odd.ratio) {
    coefOR2 <- lapply(model, function(x) exp(coef(x)))
    seOR2 <- lapply(model, function(x) exp(coef(x)) * summary(x)$coef[, 2])
    p2 <- lapply(model, function(x) summary(x)$coefficients[, 4])
    stargazer(model, coef = coefOR2, se = seOR2, p = p2, ...)
    
  } else {
    stargazer(model, ...)
  }
}
library(stargazer)
# output for the evote models
models_evote <- list(glm1_02, glm1_16, glm1_18)

stargazer2(models_evote, 
          type = "html", 
          out = "modelsevote.html",
          font.size = "small",
          align = TRUE,
          omit.stat = c("f", "ser"),
          column.sep.width = "-5pt")
## 
## <table style="text-align:center"><tr><td colspan="4" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="3"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="3" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td colspan="2">DV</td><td>DV1</td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td></tr>
## <tr><td colspan="4" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">tech</td><td>0.323<sup>*</sup></td><td>0.118<sup>***</sup></td><td>-0.016</td></tr>
## <tr><td style="text-align:left"></td><td>(0.179)</td><td>(0.022)</td><td>(0.017)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">effdmy</td><td>-4.237<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.173)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">newtech</td><td>-5.220<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.143)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">exptdmy</td><td>-0.207<sup>**</sup></td><td>1.907<sup>***</sup></td><td>0.439<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.088)</td><td>(0.054)</td><td>(0.032)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">netuse</td><td></td><td></td><td>0.029<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.010)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryBE</td><td>1.338<sup>***</sup></td><td>0.754<sup>***</sup></td><td>-0.328<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.254)</td><td>(0.106)</td><td>(0.102)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryDE-E</td><td>0.770<sup>***</sup></td><td></td><td>0.961<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.257)</td><td></td><td>(0.127)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryDE-W</td><td>0.689<sup>***</sup></td><td></td><td>0.630<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.243)</td><td></td><td>(0.101)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryBG</td><td></td><td>1.403<sup>***</sup></td><td>-0.535<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.111)</td><td>(0.109)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryCY</td><td></td><td>0.899<sup>***</sup></td><td>-0.604<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.137)</td><td>(0.134)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryCZ</td><td></td><td>1.134<sup>***</sup></td><td>0.194<sup>*</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.113)</td><td>(0.099)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryDE</td><td></td><td>0.128</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.102)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryDK</td><td>-1.021<sup>***</sup></td><td>1.558<sup>***</sup></td><td>-0.382<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.235)</td><td>(0.115)</td><td>(0.102)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryEE</td><td></td><td>1.511<sup>***</sup></td><td>0.228<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.116)</td><td>(0.102)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryES</td><td>0.921<sup>***</sup></td><td>0.704<sup>***</sup></td><td>-1.124<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.266)</td><td>(0.106)</td><td>(0.117)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryFI</td><td>-0.166</td><td>1.709<sup>***</sup></td><td>-0.601<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.227)</td><td>(0.123)</td><td>(0.104)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryFR</td><td>0.967<sup>***</sup></td><td>0.060</td><td>-0.666<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.253)</td><td>(0.101)</td><td>(0.107)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryGB-GBN</td><td>0.946<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.241)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryGB-NIR</td><td>0.342</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.322)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryGB</td><td></td><td>0.988<sup>***</sup></td><td>-0.547<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.110)</td><td>(0.105)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryGR</td><td>-0.342</td><td>0.653<sup>***</sup></td><td>-0.587<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.257)</td><td>(0.104)</td><td>(0.108)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryHR</td><td></td><td>0.408<sup>***</sup></td><td>-0.151</td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.105)</td><td>(0.100)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryHU</td><td></td><td>0.110</td><td>-0.112</td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.101)</td><td>(0.101)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryIE</td><td>0.534<sup>**</sup></td><td>1.007<sup>***</sup></td><td>-0.908<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.245)</td><td>(0.106)</td><td>(0.110)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryIT</td><td>1.257<sup>***</sup></td><td>0.184<sup>*</sup></td><td>-0.557<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.243)</td><td>(0.102)</td><td>(0.106)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryLT</td><td></td><td>1.491<sup>***</sup></td><td>-0.293<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.117)</td><td>(0.105)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryLU</td><td>0.396</td><td>0.169</td><td>-0.317<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.261)</td><td>(0.125)</td><td>(0.130)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryLV</td><td></td><td>1.207<sup>***</sup></td><td>-0.463<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.113)</td><td>(0.104)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryMT</td><td></td><td>0.944<sup>***</sup></td><td>-0.528<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.133)</td><td>(0.134)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryNL</td><td>2.498<sup>***</sup></td><td>1.083<sup>***</sup></td><td>-0.179<sup>*</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.249)</td><td>(0.111)</td><td>(0.100)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryNO</td><td>0.593<sup>**</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.237)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryPL</td><td></td><td>0.875<sup>***</sup></td><td>0.070</td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.108)</td><td>(0.100)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryPT</td><td>1.162<sup>***</sup></td><td>1.235<sup>***</sup></td><td>-0.248<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.257)</td><td>(0.115)</td><td>(0.105)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryRO</td><td></td><td>0.794<sup>***</sup></td><td>-0.272<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.105)</td><td>(0.103)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntrySE</td><td>1.090<sup>***</sup></td><td>1.585<sup>***</sup></td><td>0.178<sup>*</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.236)</td><td>(0.115)</td><td>(0.097)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntrySI</td><td></td><td>0.296<sup>***</sup></td><td>0.378<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.101)</td><td>(0.101)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntrySK</td><td></td><td>1.191<sup>***</sup></td><td>0.409<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.116)</td><td>(0.103)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">sex</td><td>0.263<sup>***</sup></td><td>-0.086<sup>***</sup></td><td>-0.238<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.084)</td><td>(0.032)</td><td>(0.029)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">age</td><td>0.003</td><td>-0.035<sup>***</sup></td><td>-0.004<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.003)</td><td>(0.001)</td><td>(0.001)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">educ</td><td>-0.002</td><td>-0.003<sup>***</sup></td><td>-0.0003</td></tr>
## <tr><td style="text-align:left"></td><td>(0.002)</td><td>(0.001)</td><td>(0.001)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Constant</td><td>13.502<sup>***</sup></td><td>2.017<sup>***</sup></td><td>-0.210<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.533)</td><td>(0.118)</td><td>(0.104)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td colspan="4" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>7,045</td><td>25,376</td><td>23,336</td></tr>
## <tr><td style="text-align:left">Log Likelihood</td><td>-1,902.306</td><td>-12,439.450</td><td>-14,112.280</td></tr>
## <tr><td style="text-align:left">Akaike Inf. Crit.</td><td>3,854.612</td><td>24,944.890</td><td>28,294.550</td></tr>
## <tr><td colspan="4" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="3" style="text-align:right"><sup>*</sup>p<0.1; <sup>**</sup>p<0.05; <sup>***</sup>p<0.01</td></tr>
## </table>
models_expat <- list(glm2_02, glm2_16, glm3_18)

stargazer2(models_expat, 
          type = "html", 
          out = "modelsexpat.html",
          font.size = "small",
          align = TRUE,
          omit.stat = c("f", "ser"),
          column.sep.width = "-5pt")
## 
## <table style="text-align:center"><tr><td colspan="4" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="3"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="3" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td colspan="3">exptdmy</td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td></tr>
## <tr><td colspan="4" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">tech</td><td>0.056</td><td>0.020</td><td>0.027</td></tr>
## <tr><td style="text-align:left"></td><td>(0.101)</td><td>(0.021)</td><td>(0.018)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">effdmy</td><td>0.030</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.087)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">newtech</td><td>-0.003</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.050)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">evotedmy</td><td>-0.278<sup>***</sup></td><td>1.928<sup>***</sup></td><td>0.441<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.076)</td><td>(0.054)</td><td>(0.033)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">netuse</td><td></td><td></td><td>-0.417<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.012)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryBE</td><td>-0.828<sup>***</sup></td><td>-0.156</td><td>0.873<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.140)</td><td>(0.109)</td><td>(0.104)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryDE-E</td><td>-0.677<sup>***</sup></td><td></td><td>-0.034</td></tr>
## <tr><td style="text-align:left"></td><td>(0.143)</td><td></td><td>(0.137)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryDE-W</td><td>-1.211<sup>***</sup></td><td></td><td>-0.380<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.152)</td><td></td><td>(0.110)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryBG</td><td></td><td>-0.858<sup>***</sup></td><td>0.178</td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.116)</td><td>(0.109)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryCY</td><td></td><td>-0.304<sup>**</sup></td><td>0.577<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.134)</td><td>(0.133)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryCZ</td><td></td><td>0.020</td><td>0.503<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.107)</td><td>(0.104)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryDE</td><td></td><td>-0.108</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.113)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryDK</td><td>0.260<sup>**</sup></td><td>-0.533<sup>***</sup></td><td>0.936<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.127)</td><td>(0.112)</td><td>(0.103)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryEE</td><td></td><td>-0.759<sup>***</sup></td><td>1.477<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.117)</td><td>(0.115)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryES</td><td>-1.244<sup>***</sup></td><td>-0.470<sup>***</sup></td><td>0.279<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.179)</td><td>(0.113)</td><td>(0.105)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryFI</td><td>0.478<sup>***</sup></td><td>0.005</td><td>1.059<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.130)</td><td>(0.109)</td><td>(0.105)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryFR</td><td>-0.537<sup>***</sup></td><td>-0.085</td><td>0.464<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.135)</td><td>(0.111)</td><td>(0.104)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryGB-GBN</td><td>-0.717<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.141)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryGB-NIR</td><td>-0.680<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.191)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryGB</td><td></td><td>-0.241<sup>**</sup></td><td>0.882<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.110)</td><td>(0.106)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryGR</td><td>-1.460<sup>***</sup></td><td>-0.882<sup>***</sup></td><td>-0.150</td></tr>
## <tr><td style="text-align:left"></td><td>(0.146)</td><td>(0.121)</td><td>(0.108)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryHR</td><td></td><td>-0.106</td><td>0.761<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.111)</td><td>(0.105)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryHU</td><td></td><td>-0.187<sup>*</sup></td><td>-0.351<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.113)</td><td>(0.108)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryIE</td><td>-0.617<sup>***</sup></td><td>-0.508<sup>***</sup></td><td>0.645<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.145)</td><td>(0.113)</td><td>(0.103)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryIT</td><td>-0.984<sup>***</sup></td><td>-0.126</td><td>-0.155</td></tr>
## <tr><td style="text-align:left"></td><td>(0.140)</td><td>(0.111)</td><td>(0.105)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryLT</td><td></td><td>-0.475<sup>***</sup></td><td>1.082<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.111)</td><td>(0.112)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryLU</td><td>-0.576<sup>***</sup></td><td>-0.033</td><td>0.193</td></tr>
## <tr><td style="text-align:left"></td><td>(0.148)</td><td>(0.134)</td><td>(0.128)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryLV</td><td></td><td>-0.125</td><td>1.147<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.108)</td><td>(0.107)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryMT</td><td></td><td>-1.317<sup>***</sup></td><td>0.841<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.169)</td><td>(0.136)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryNL</td><td>-0.492<sup>***</sup></td><td>-0.261<sup>**</sup></td><td>1.596<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.148)</td><td>(0.109)</td><td>(0.109)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryNO</td><td>-0.005</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.137)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryPL</td><td></td><td>-0.148</td><td>0.659<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.109)</td><td>(0.107)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryPT</td><td>-1.015<sup>***</sup></td><td>-0.606<sup>***</sup></td><td>0.007</td></tr>
## <tr><td style="text-align:left"></td><td>(0.164)</td><td>(0.114)</td><td>(0.109)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntryRO</td><td></td><td>-1.202<sup>***</sup></td><td>0.049</td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.131)</td><td>(0.107)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntrySE</td><td>0.010</td><td>-0.792<sup>***</sup></td><td>0.282<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.133)</td><td>(0.115)</td><td>(0.101)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntrySI</td><td></td><td>-0.412<sup>***</sup></td><td>0.303<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.115)</td><td>(0.109)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">isocntrySK</td><td></td><td>-0.093</td><td>0.724<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.108)</td><td>(0.113)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">sex</td><td>0.073</td><td>0.184<sup>***</sup></td><td>-0.041</td></tr>
## <tr><td style="text-align:left"></td><td>(0.050)</td><td>(0.032)</td><td>(0.030)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">age</td><td>0.005<sup>***</sup></td><td>0.010<sup>***</sup></td><td>-0.021<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.002)</td><td>(0.001)</td><td>(0.001)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">educ</td><td>0.002</td><td>-0.001</td><td>-0.001</td></tr>
## <tr><td style="text-align:left"></td><td>(0.001)</td><td>(0.001)</td><td>(0.001)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Constant</td><td>-0.209</td><td>-3.334<sup>***</sup></td><td>1.273<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.273)</td><td>(0.132)</td><td>(0.105)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td></tr>
## <tr><td colspan="4" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>7,045</td><td>25,376</td><td>23,336</td></tr>
## <tr><td style="text-align:left">Log Likelihood</td><td>-4,762.612</td><td>-12,721.640</td><td>-13,403.640</td></tr>
## <tr><td style="text-align:left">Akaike Inf. Crit.</td><td>9,575.225</td><td>25,509.270</td><td>26,877.290</td></tr>
## <tr><td colspan="4" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="3" style="text-align:right"><sup>*</sup>p<0.1; <sup>**</sup>p<0.05; <sup>***</sup>p<0.01</td></tr>
## </table>

Wrap up

sessionInfo()
## R version 4.1.0 (2021-05-18)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur 10.16
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] sjPlot_2.8.10       sjstats_0.18.1      blorr_0.3.0        
##  [4] modelsummary_0.11.0 psych_2.2.5         stargazer_5.2.3    
##  [7] gmodels_2.18.1.1    survey_4.1-1        lme4_1.1-29        
## [10] Matrix_1.3-3        dplyr_1.0.9         pscl_1.5.5         
## [13] Hmisc_4.7-0         ggplot2_3.3.6       Formula_1.2-4      
## [16] survival_3.2-11     lattice_0.20-44     haven_2.5.0        
## [19] DescTools_0.99.45  
## 
## loaded via a namespace (and not attached):
##   [1] readxl_1.4.0        backports_1.4.1     systemfonts_1.0.4  
##   [4] splines_4.1.0       listenv_0.8.0       TH.data_1.1-1      
##   [7] digest_0.6.29       htmltools_0.5.2     gdata_2.18.0.1     
##  [10] fansi_1.0.3         magrittr_2.0.3      checkmate_2.1.0    
##  [13] cluster_2.1.2       see_0.7.0           tzdb_0.3.0         
##  [16] globals_0.15.0      readr_2.1.2         modelr_0.1.8       
##  [19] sandwich_3.0-1      svglite_2.1.0       jpeg_0.1-9         
##  [22] colorspace_2.0-3    rvest_1.0.2         mitools_2.4        
##  [25] xfun_0.31           crayon_1.5.1        jsonlite_1.8.0     
##  [28] Exact_3.1           zoo_1.8-10          glue_1.6.2         
##  [31] kableExtra_1.3.4    gtable_0.3.0        emmeans_1.7.4-1    
##  [34] webshot_0.5.3       sjmisc_2.8.9        car_3.0-13         
##  [37] future.apply_1.9.0  abind_1.4-5         scales_1.2.0       
##  [40] mvtnorm_1.1-3       DBI_1.1.2           ggeffects_1.1.2    
##  [43] Rcpp_1.0.8.3        viridisLite_0.4.0   xtable_1.8-4       
##  [46] performance_0.9.0   htmlTable_2.4.0     tmvnsim_1.0-2      
##  [49] foreign_0.8-81      proxy_0.4-26        datawizard_0.4.1   
##  [52] htmlwidgets_1.5.4   httr_1.4.3          RColorBrewer_1.1-3 
##  [55] ellipsis_0.3.2      pkgconfig_2.0.3     farver_2.1.0       
##  [58] nnet_7.3-16         sass_0.4.1          utf8_1.2.2         
##  [61] tidyselect_1.1.2    labeling_0.4.2      rlang_1.0.2        
##  [64] effectsize_0.7.0    munsell_0.5.0       cellranger_1.1.0   
##  [67] tools_4.1.0         cli_3.3.0           generics_0.1.2     
##  [70] sjlabelled_1.2.0    broom_0.8.0         evaluate_0.15      
##  [73] stringr_1.4.0       fastmap_1.1.0       yaml_2.3.5         
##  [76] tables_0.9.6        knitr_1.39          purrr_0.3.4        
##  [79] rootSolve_1.8.2.3   future_1.26.1       nlme_3.1-152       
##  [82] xml2_1.3.3          compiler_4.1.0      rstudioapi_0.13    
##  [85] png_0.1-7           e1071_1.7-11        tibble_3.1.7       
##  [88] bslib_0.3.1         stringi_1.7.6       highr_0.9          
##  [91] parameters_0.18.1   forcats_0.5.1       nloptr_2.0.3       
##  [94] vctrs_0.4.1         pillar_1.7.0        lifecycle_1.0.1    
##  [97] lmtest_0.9-40       jquerylib_0.1.4     estimability_1.3   
## [100] data.table_1.14.2   insight_0.17.1      lmom_2.9           
## [103] R6_2.5.1            latticeExtra_0.6-29 gridExtra_2.3      
## [106] parallelly_1.31.1   gld_2.6.4           codetools_0.2-18   
## [109] boot_1.3-28         MASS_7.3-54         gtools_3.9.2.1     
## [112] assertthat_0.2.1    withr_2.5.0         mnormt_2.0.2       
## [115] multcomp_1.4-19     bayestestR_0.12.1   expm_0.999-6       
## [118] parallel_4.1.0      hms_1.1.1           rpart_4.1-15       
## [121] tidyr_1.2.0         coda_0.19-4         class_7.3-19       
## [124] minqa_1.2.4         rmarkdown_2.14      carData_3.0-5      
## [127] base64enc_0.1-3
Sys.time()
## [1] "2022-06-21 00:08:43 CEST"
# note how long the script takes to (re-)run
Sys.time() - start
## Time difference of 38.41479 mins